Wearable intelligent throat enables natural speech in stroke patients with dysarthria

Domain: neuroscience
Published: 2026-01-19
Editorial outcome: major_revision

Abstract
Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven intelligent throat (IT) system that integrates throat muscle vibrations and carotid pulse signals sensors with large language model (LLM) processing to enable fluent, emotionally expressive communication. The system utilizes ultrasensitive textile strain sensors to capture high-quality signals from the neck area and supports token-level processing for real-time, continuous speech decoding, enabling seamless, delay-free communication. In tests with five stroke patients with dysarthria, IT’s LLM agents intelligently corrected token errors and enriched sentence-level emotional and logical coherence, achieving low error rates (4.2% word error rate, 2.9% sentence error rate) and a 55% increase in user satisfaction. This work establishes a portable, intuitive communication platform for patients with dysarthria with the potential to be applied broadly across different neurological conditions and in multi-language support systems.

Introduction
Neurological diseases such as stroke, amyotrophic lateral sclerosis (ALS), and Parkinson’s disease frequently result in dysarthria—a severe motor-speech disorder that compromises neuromuscular control over the vocal tract. This impairment drastically restricts effective communication, lowers quality of life, substantially impedes the rehabilitation process, and can even lead to severe psychological issues1–4. Augmentative and alternative communication (AAC) technologies have been developed to address these challenges, including letter-by-letter spelling systems utilizing head or eye tracking5–8 and neuroprosthetics powered by brain-computer interface (BCI) devices9–12. While head or eye tracking systems are relatively straightforward to implement, they suffer from slow communication speeds. Neuroprosthetics, while transformative for severe paralysis cases, often rely on invasive, complex recordings and processing of neural signals. For individuals retaining partial control over laryngeal or facial muscles, a strong need remains for solutions that are more intuitive and portable (Supplementary Note 1).

A promising solution lies in wearable silent speech devices that capture non-acoustic signals, such as subtle skin vibrations13–17 or electrophysiological signals from the speech motor cortex18–21. These technologies offer non-invasiveness, comfort, and portability, with potential for seamless daily integration. Yet, despite their promise, current wearable silent speech systems still face three fundamental limitations that hinder their clinical translation and real-world usability. First, most existing studies have been validated primarily on healthy participants, with limited exploration of patient accessibility and adaptability. The resulting gap between laboratory validation and patient-specific deployment prevents these systems from serving individuals with dysarthria or other speech impairments in everyday contexts13–15. Second, previous systems often restrict user expression to discrete, word-level decoding within fixed time windows, requiring users to pause and wait before articulating the next word. Such fragmented temporal segmentation disrupts the natural rhythm of silent articulation and makes fluid, continuous communication nearly impossible13–17. Third, most approaches rely on a 1:1 mapping between silent articulatory inputs and text outputs. While this direct correspondence works for healthy users, it places excessive physical and cognitive strain on patients, who often experience fatigue even when silently articulating longer sentences (Supplementary Video 1)13–17. For these users, a system capable of intelligently expanding shorter or incomplete expressions into coherent, emotionally aligned sentences is crucial for restoring both efficiency and naturalness in communication.

To advance wearable silent speech systems for real-world dysarthria patient use, we developed an AI-driven intelligent throat (IT) system that captures extrinsic laryngeal muscle vibrations and carotid pulse signals, integrating silent speech and emotional states analysis in real-time. The system generates personalized, contextually appropriate sentences that accurately reflect patients’ intended meaning (Fig. 1). It employs ultrasensitive textile strain sensors, fabricated using advanced printing techniques, to ensure comfortable, durable, and high-quality signal acquisition14,22. By analyzing speech signals at the token level (~100ms), our approach outperforms traditional time-window methods, enabling continuous, fluent word and sentence expression in real time. Knowledge distillation further reduces computational latency by 76%, significantly enhancing communication fluidity. Large language models (LLMs) serve as intelligent agents, automatically correcting token classification errors and generating personalized, context-aware speech by integrating emotional states and environmental cues. Pre-trained on a dataset from 10 healthy individuals, the system achieved a word error rate (WER) of 4.2% and a sentence error rate (SER) of 2.9% when fine-tuned on data from five dysarthric stroke patients. Additionally, the integration of emotional states and contextual cues further personalizes and enriches the decoded sentences, resulting in a 55% increase in user satisfaction and enabling dysarthria patients to communicate with fluency and naturalness comparable to that of healthy individuals. Table S1 provides a comprehensive comparison between the IT system and state-of-the-art wearable silent speech systems.

Results
The intelligent throat system
The IT system consists primarily of hardware (a smart choker embedding textile strain sensors and a wireless readout printed circuit board (PCB)) and software components (machine learning models and LLM agents). Silent speech signals generated in real time by the user’s silent expressions (silently mouthed words in the absence of vocalized sound) are decoded by a token decoding network and synthesized into an initial sentence by the token synthesis agent (TSA). Simultaneously, pulse signals are collected from the smart choker device and processed by an emotion decoding network to determine the user’s real-time emotional status. The sentence expansion agent (SEA) intelligently expands the TSA-generated sentence, incorporating personalized emotion labels and objective contextual background data to produce a refined, emotionally expressive, and logically coherent sentence that captures the user’s intended meaning (Fig. 1, Supplementary Video 2). Each component of the IT system is elaborated upon in the following sections.

Figure 2a shows the structure of the strain sensing choker screen-printed on an elastic knitted textile (Supplementary Note 3). The choker features two channels located at the front and side of the neck, designed to monitor the strain applied to the skin by the muscles near the throat and the carotid artery (Supplementary Fig. 1). The graphene layer printed on the textile forms ordered cracks along the stress concentration areas of the textile lattice to detect subtle skin vibrations14. Silver electrodes are connected to the integrated PCB on the choker. A rigid strain isolation layer with high Young’s modulus is printed around each channel to reduce crosstalk between the two channels and the variable strain caused by wearing. To further validate this effect, we compared devices with and without the isolation layer under identical stretching conditions (Supplementary Fig. 21), confirming that the isolation layer markedly suppresses strain transfer. Due to the difference in Young’s modulus between the elastic textile substrate and the strain isolation layer, less than 1% of external strain is transmitted to the interior when wearing the choker, while the internal sensing areas remain soft and elastic (Supplementary Fig. 2)22. Furthermore, to quantitatively validate the anisotropic strain sensitivity, we measured the sensor’s responses under x-, y-, and z-axis deformation (Supplementary Fig. 20), confirming that the intended x-axis strain dominates the signal while cross-axis interference remains negligible. For uniaxial stretching (x-axis) from 1-10Hz, the printed textile-based graphene strain sensor shows good linear behavior, producing a response over 10% to subtle strains of 0.1% and maintaining a gauge factor (GF) over 100 during high-frequency stretching (Fig. 2b), while y- and z-axis deformations contribute negligible signal variations due to the anisotropic crack propagation mechanism. Based on our previous findings and related studies, the 0.1% strain threshold has been validated as sufficient for capturing silent speech-induced muscle vibrations14,15,17. Furthermore, our previous studies have confirmed the reliability of the printed textile-based strain sensors with high robustness, durability, and washability, as well as high levels of comfort, biocompatibility, and breathability14,22.

To operate the system and enable wireless communication between the IT choker and server, the PCB was designed for bi-channel measurements (i.e., silent speech and carotid pulse signals), enabling simultaneous acquisition of speech and emotional cues. The PCB integrates a low-power Bluetooth module (Fig. 2c) for continuous data transmission while optimizing energy efficiency for extended use. Key components of the PCB include an analog-to-digital converter (ADC) for high-fidelity signal digitization and a microcontroller unit (MCU) that manages data processing and transmission (Fig. 2d, Supplementary Fig. 4, and Supplementary Fig. 5). Power supply, operational amplifiers, and the reference voltage chip are configured to ensure stable signal amplification, catering to the sensitivity requirements of both strain and pulse sensors. For the energy management system, a comprehensive power budget analysis reveals that the designed PCB operates with a total power consumption of 76.5 mW (Fig. 2e). The main power-consuming components are the Bluetooth module (29.7 mW) and amplification circuits (31.9 mW). To extend operational time and support portable use, a 1800 mWh battery was incorporated, providing sufficient capacity for continuous operation throughout an entire day without recharging.

Token-level speech decoding
Current wearable silent speech systems operate by recognizing discrete words or predefined sentences and lack the ability for continuous, real-time expression analysis typical of the human brain23. This limitation arises because these systems rely on fixed time windows (typically 1–3 seconds) for word decoding, requiring users to complete each word within a set interval and pause until the next window to continue13–21. Such constraints lead to fragmented expression and unnatural user experience. To address this, we developed a high-resolution tokenization method for signal segmentation (Fig. 2f), dividing speech signals into fine-grained ~100ms segments for continuous word label recognition. This granular segmentation ensures that each token accurately corresponds to a specific part of a single word and is labeled accordingly. This setup enables users to speak fluidly without worrying about timing constraints, as the system continuously classifies and aggregates tokens into coherent words and sentences. Our optimization determined that a token length of 144ms offers the ideal balance: it minimizes boundary confusion from longer tokens while avoiding the increased computational demands associated with shorter tokens. This value was empirically determined by gradually reducing token length from 200ms while monitoring the proportion of boundary-crossing tokens (tokens spanning two adjacent words). A threshold of <5% boundary-crossing tokens was used to define acceptable boundary stability. Shorter tokens were not adopted because the small residual ambiguities they eliminate can already be corrected by the TSA, which applies contextual reasoning and majority voting during word reconstruction. This fine-grained segmentation not only eliminates the unnatural pauses imposed by prior fixed-time-window methods but also ensures that each token retains essential local signal features. Compared to tradition silent speech decoding approaches, which rely on whole-word classification, this token-based approach enables a real-time, continuous speech experience that more closely mimics natural spoken language.

While high-resolution tokenization improves fluidity, shorter tokens inherently contain limited context, making them less effective for accurate word decoding. Temporal machine learning models, like recurrent neural networks (RNN) or transformers, could capture contextual dependencies, but their complexity and computational cost render them suboptimal for wearable silent speech systems24–26, which prioritize real-time operation. To balance context awareness and computational efficiency, we implemented an explicit context augmentation strategy (Fig. 3a), where each sample consists of N tokens: N-1 preceding tokens provide context, and the current token determines the sample’s label. For initial tokens, any missing preceding tokens are padded with blank tokens to ensure completeness. We found N=15 tokens to be optimal (Fig. 3c), with accuracy initially increasing as tokens accumulate, then declining due to insufficient context at lower counts and gradient decay or information loss at higher counts27. This strategy enables the use of efficient one-dimensional convolutional neural networks (1D-CNNs) instead of computationally intensive temporal models for token decoding28,29. Attention maps reveal that signals from preceding regions indeed contribute to token decoding, validating the effectiveness of the explicit context augmentation strategy (Supplementary Fig. 10).

To further enhance model efficiency and accuracy on patients’ data, we designed the training pipeline shown in Fig. 3b. The model was pre-trained on a larger dataset from healthy individuals and then fine-tuned on the limited patients’ data, leveraging shared signal features to enhance patient-specific decoding. After only 25 repetitions per word in few-shot learning, the model achieved a token classification accuracy of 92.2% (Fig. 3d). In contrast, a model trained from scratch using solely patients’ data could only reach an accuracy of 79.8%. Additionally, we employed response-based knowledge distillation30 to transfer knowledge from a larger 1D ResNet-101 model to a smaller 1D ResNet-18, reducing computational load by 75.6% while maintaining high accuracy, with only a 0.9% drop from the teacher model, achieving 91.3% (Fig. 3e). In the inference stage, each segmented token is processed by this trained 1D ResNet-18 model (the final token decoding network) to generate token labels that serve as inputs to the TSA.

Figure 3f, g display the confusion matrix and UMAP feature visualization for token decoding31. Over 90% of the classification errors involved confusion between class 0 (blank tokens) and neighboring word tokens. As shown in later analyses of the LLM agent’s performance, such boundary errors can be effectively corrected during token-to-word synthesis by the TSA. This knowledge distillation and transfer learning framework ensures that computational efficiency is maximized without sacrificing accuracy. Unlike prior approaches that train models from scratch on small patient datasets, our pipeline generalizes well across individuals, addressing a key challenge in real-world silent speech decoding for dysarthric patients.

To further evaluate the discriminability of the IT system on visually and articulatorily similar word pairs, we analyzed five viseme-similar pairs (increase/decrease, ship/sheep, book/look, metal/medal, and dessert/desert). The model achieved an average per-word accuracy of 96.3%, with pairwise confusion rates below 8%, indicating that the system can reliably distinguish between look-alike mouth shapes and subtle articulatory gestures. The detailed confusion matrix is shown in Supplementary Fig. 16. To understand how the system achieves such discriminability, we visualized the raw strain signals and Grad-CAM relevance maps for representative word pairs. As shown in Supplementary Fig. 17, the model consistently focuses on the key articulatory segments where the target words diverge, such as the onset regions in the dessert/desert or book/look pairs. These attention maps confirm that the predictions are driven by meaningful physiological patterns rather than incidental noise or silence segments.

Decoding of emotional states
To enrich sentence coherence by providing emotional context, we decode emotional states from carotid pulse signals. Emotional changes modulate autonomic nervous activity, which in turn alters the temporal structure of the R-R interval (RRI) within pulse signals, forming measurable physiological representations of affective states32. Our machine learning model establishes a direct mapping between these RRI-based temporal representations and corresponding emotional categories. Emotion recognition can be achieved through a range of modalities, including facial expression, audio cues, electromyography, and other physiological signals such as heart rate and blood pressure33–35. While multimodal approaches may offer improved accuracy in general populations, they often require additional sensors, power, and computation, limiting system wearability and daily usability. In line with our objective of developing a compact, fully wearable system, we opted for a single-modality strategy centered on carotid pulse signals. This choice reflects a deliberate trade-off between integration and signal diversity. Specifically, stroke patients, our target users, typically exhibit limited mobility, which mitigates motion artifacts and stabilizes pulse dynamics. These conditions allow short-duration pulse segments to provide sufficiently discriminative features for emotion decoding, as demonstrated in our results. Therefore, our use of pulse-based emotion inference is not only aligned with the engineering goals of system simplicity and comfort, but also grounded in the physiological characteristics of the intended clinical population.

Using 5-second windows, we segmented patients’ pulse signals into samples to construct a dataset, focusing on three common emotion categories for stroke patients: neutral, relieved, and frustrated (data collection protocol detailed in Methods). Figure 4a shows the discrete Fourier transform (DFT) distributions for each emotion, highlighting distinct frequency characteristics among these emotional states. Accordingly, we incorporated DFT frequency extraction into the decoding pipeline shown in Fig. 4b, where removal of the DC component, Z-score normalization, and DFT are sequentially applied before feeding the values into a classifier for categorization. The DFT-based approach was selected for its ability to represent key characteristics of carotid pulse signals, including power distribution, frequency-domain features, and waveform morphology, within a single transformation. This method enables our end-to-end neural network to automatically extract the most relevant features for emotion classification, eliminating the need for manual feature engineering. Figure 4c illustrates the performance of different classifiers with and without DFT frequency extraction. The results show a significant improvement in decoding accuracy with DFT. The optimal model was the 1D-CNN with DFT, achieving an accuracy of 83.2%, with its confusion matrix displayed in Fig. 4d. The SHAP values reveal that the emotion decoding model primarily focuses on low-frequency signals in the 0-2Hz range, which is consistent with the pulse signal range demonstrated by the DFT (Supplementary Fig. 11).

In addition to the silent speech and carotid pulse signals analyzed in this study, various physiological activities generate distinct vibrational signals in the neck area, which can introduce artifacts hindering analysis36,37. Figure 4e shows the frequency and magnitude distributions of several prominent signals in this region. Our observations revealed that silent speech exhibits a relatively strong magnitude, and in applications with the IT, vibration can propagate transversely from the throat center to the carotid artery, introducing crosstalk in the pulse signal. Due to the considerable frequency overlap between silent speech and pulse signals, digital filters are non-ideal for effective artifacts suppression38. While adding reference channels could theoretically help, it does not align with the goal of a highly integrated IT39. To address this issue, we employed a stress isolation treatment using a polyurethane acrylate (PUA) layer, as shown in Fig. 2a, to prevent strain crosstalk propagation along the IT. The theoretical basis of this isolation strategy has been thoroughly discussed in our previous study22. Figure 4f compares pulse signals with and without strain isolation treatment when silent speech occurs concurrently (the vowel “a” introduced at 2.5s), demonstrating significant crosstalk resilience in the treated IT, with the signal-to-interference ratio improved by more than 20 dB.

LLM agents for sentence synthesis and intelligent expansion
During clinical observations, we found that stroke patients often experienced marked fatigue even when silently mouthing short phrases, making sustained or complex utterances impractical. To reduce physical effort while preserving the intended message, we incorporated an intelligent expansion option that allows patients to express concise tokens, which are then automatically enriched into complete, contextually appropriate sentences.

To naturally and coherently synthesize sentences that accurately reflect the patient’s intended expression from the decoded token and emotion labels, we introduced two LLM agents based on the GPT-4o-mini API (Fig. 5a, Supplementary Note 4): the token synthesis agent (TSA) and the sentence expansion agent (SEA). The TSA merges token labels directly into words silently expressed by the patient and combines them into sentences (left). During this process, it intelligently aggregates consecutive token predictions based on contextual consistency and performs majority-voting reasoning to correct occasional decoding errors or boundary ambiguities from the token decoding network, thereby ensuring accurate word-level reconstruction before sentence formation. The SEA, on the other hand, leverages emotion labels and objective information, such as time and weather, to expand these basic sentences into logically coherent, personalized expression that better capture the patient’s true intent. Through a simple interaction (in this study, two consecutive nods), patients can flexibly choose between direct output and expanded sentences, ensuring that expansion is used only when it aligns with their communication needs.

To optimize the performance of the TSA, we refined the prompt design40. First, we optimized the prompt length (Fig. 5b), observing a trend where both WER and SER improved with increasing prompt length up to 400 words before eventually deteriorating for higher lengths. We attribute this trend to the fact that longer prompts provide clearer synthesis instructions, but overly lengthy prompts dilute the model’s focus ability. Additionally, we compared performance with and without example cases, where the agent was provided with five examples of token label sequences and their corrected word outputs. Including examples significantly improved synthesis accuracy (Fig. 5c). Finally, we evaluated the effect of providing empirical constraints, which specify typical token counts for words of various lengths. Performance improved considerably when constraints were included (Fig. 5d). Under optimal prompt conditions, TSA achieved its best performance with a WER of 4.2% and an SER of 2.9%.

We also assessed and refined the performance of the SEA. Patient satisfaction with the expanded sentences was evaluated through a questionnaire (see Table S4 for criteria details). Following Chain-of-Thought (CoT) optimization41 and the inclusion of patient-provided expansion examples, the expanded sentences scored significantly higher across multiple criteria (Fig. 5f). Contribution analysis revealed that emotion labels made a substantial impact on emotion accuracy, while objective information notably improved fluency, jointly contributing to the overall satisfaction with the expanded sentences compared to the basic word-only output (Fig. 5e). Under optimal prompt conditions, the SEA-generated expanded sentences resulted in a 55% increase in overall patient satisfaction compared to the TSA’s direct output, raising satisfaction from “somewhat satisfied” to “fully satisfied” levels (Supplementary Fig. 12 and Supplementary Fig. 13). As shown in Fig. 5f, the core meaning metric remains stable across all sentence expansion conditions. This stability stems from the high accuracy of the token decoding model and TSA, which ensure precise word recognition and correct token synthesis. Since core meaning reflects whether the fundamental subject-verb-object (SVO) structure aligns with the user’s intended message, this metric remains largely unchanged after expansion. However, as illustrated in Fig. 5e, additional contextual information—including objective data (e.g., time, weather) and emotion labels—enriches fluency and personalization, significantly improving overall user satisfaction. In both operating modes, sentences generated by the TSA and SEA agents are sent to an open-source text-to-speech model42, which synthesizes audio that matches the patient’s natural voice for playback. In real-world applications, the delay between the completion of the user’s silent expression and the sentence playback is approximately 1 second (Supplementary Note 2). This low latency effectively supports seamless and natural communication in practical settings. To assess the long-term adaptability of the IT system, we conducted a follow-up test six months after initial training, observing an increase in WER due to changes in neuromuscular control, which was rapidly restored to initial performance levels after a brief few-shot fine-tuning (five repetitions per words) session (Table S5).

Methods
Materials
TIMREX KS 25 Graphite (particle size of 25μm) was sourced from IMERYS. Stretchable conductive silver ink was obtained from Dycotec Materials Ltd. Ethyl cellulose was purchased from SIGMA-ALDRICH. Flexible UV Resin Clear was acquired from Photocentric Ltd. The textile substrate, composed of 95% Polyester and 5% spandex, was procured from Jelly Fabrics Ltd.

Ink formulation
The graphene ink for screen printing was prepared following a reported method. Briefly, 100g of graphite powder and 2g of ethyl cellulose (EC) were mixed in 1L of isopropyl alcohol (IPA) and stirred at 3000 rpm for 30 min. The mixture was then added into a high-pressure homogenizer (PSI-40) at 2000 bar pressure for 50 cycles to obtain graphene dispersion. The graphene dispersion is centrifuged at 5000 × g for 30 min to remove unexfoliated graphite.

Fabrication of textile strain sensor
The textile substrate was washed with detergent, thoroughly dried, and then treated with UV-ozone for 5 min to clean the surface. Screen printing was performed using a 165T polyester silk screen on a semi-automatic printer (Kippax & Sons Ltd.) set with a squeegee angle of 45 degrees, a spacer of 2 mm, a coating speed of 10 mm/s, and a printing speed of 40 mm/s. Graphene ink, silver paste, and PUA were successively printed to form the sensing layer, electrodes, and strain isolation layer, respectively. After printing the PUA, the textile was exposed to UV light for 5 minutes. After each printing pass, the textile was air-dried. Following printing, the sensor was dried at 80 °C overnight. A biaxial strain of approximately 10% was then applied to induce the formation of ordered cracks.

Following the standardized fabrication protocol described in our previous work14,22, our strain sensors exhibit high device-to-device consistency. Across 50 independently fabricated units, the gauge factor (GF) remains consistently centered around ~100, confirming uniform performance. To further verify its applicability across the full signal frequency range involved in pulse (0-10Hz) and silent-speech (up to ~100Hz) detection, we evaluated the frequency-dependent strain response of the printed sensor (Supplementary Fig. 19). The GF remained above 98 even at 150Hz, confirming stable high sensitivity under high-frequency dynamic deformation. Furthermore, long-term stability tests conducted over one month showed no significant degradation in sensor performance, demonstrating the robustness of the sensing mechanism for extended use.

A quantitative head-to-head comparison with MEMS and PVDF sensors under identical conditions further confirmed the superior signal fidelity and comfortability of the proposed textile strain sensor (Table S9). In addition, to evaluate mechanical durability under wearable conditions, printed strips with the same multilayer structure were subjected to 5000 cycles of 0.1–1% tensile loading and to 5000 cycles of bending at ~5mm radius (Supplementary Fig. 18), both showing stable ΔR/R responses without noticeable degradation.

Characterization
Scanning Electron Microscopy (SEM) images were taken with a Magellan 400, after sputtering the textile samples with a 5 nm layer of gold to enhance conductivity. Optical images were captured using an Olympus microscope. Tensile properties of the textile strain sensors were evaluated using a Deben Microtest 200N Tensile Stage and an INSTRON universal testing system. Electrical signals were recorded concurrently with a potentiostat (EmStat4X, PalmSens) and a multiplexer (MUX, PalmSens). Copper tape was crimped onto the contact pads of the samples, supplemented with a small amount of silver paste to improve electrical contact.

Wireless PCB for data readout
A custom wireless PCB was developed for efficient, continuous data acquisition and transmission within the IT system. Powered by a TP4065 lithium charger and a 3.3V regulator, the PCB ensures stable operation via battery or USB. The STM32G431 microcontroller captures silent speech and carotid pulse signals through two ADC channels, with an OPA2192 operational amplifier for high-precision signal conditioning, amplifying low-level signals and enhancing overall data fidelity. A BLE module (BLE-SER-A-ANT) transmits real-time data via UART, enabling seamless, delay-free communication.

To evaluate the comfortability of the wearable system, we conducted a subjective rating assessment with five stroke patients. Each patient rated the system on four key aspects—weight, fit, material flexibility, and long-term wearability—on a 1 to 5 scale (1=very uncomfortable, 5=very comfortable). The results are summarized in Table S6, where the overall comfort score was averaged across the four categories. These ratings indicate that the device was generally well-tolerated, with an average overall comfort score of 4.0, though some patients expressed concerns regarding long-term wearability due to rigid PCB components. Future work will focus on further miniaturization and material optimizations to enhance wearability.

To enhance the integration of the IT system and improve overall wearability, a reinforced nylon outer shell was added around the textile choker, providing structural stability without compromising flexibility. The conductive yarns were routed through the gap between the textile choker and the outer casing to connect seamlessly with the PCB. This design modification enhances compactness while ensuring stable signal transmission. The structural improvements and practical wearability of this design are demonstrated in Supplementary Video 3. Future iterations will focus on further miniaturization through the adoption of flexible printed circuit boards (FPCs) and fully integrated wireless communication modules, which will allow direct embedding of the electronics within the textile choker, eliminating the need for external wired connections.

Assembly and integration of the signal acquisition system
The signal acquisition device consists of a wireless readout module and textile strain sensors. The assembly and wearing configuration are shown in Supplementary Fig. 15. The wireless module was fabricated by enclosing the PCB within a 3D-printed housing for protection and mechanical stability. Electrical contacts on the bottom of the module were extended using copper foil tape to facilitate subsequent connection to the textile sensors.

The textile strain sensors were mounted on an elastic band and connected to the wireless module through conductive yarns stitched laterally along the fabric surface. These conductive yarns pass through the textile layer and are directly linked to the copper foil contacts at the bottom of the wireless device, ensuring reliable electrical connection and mechanical flexibility. This design allows the two sensing channels positioned at the front and side of the neck to interface seamlessly with the wireless PCB positioned on the opposite side, forming a compact, lightweight, and balanced wearable configuration.

Strain isolation design to control the initial strain set
To account for variability in choker tightness caused by differences in neck circumferences, we implemented a strain isolation strategy similar to the approach used in our previous study22. Specifically, a PUA layer with a high Young’s modulus was printed around the active sensing area to reduce the influence of tensile deformation introduced by different neck sizes. This layer functions as a strain isolation mechanism, ensuring that the sensor’s baseline resistance remains stable across different users by preventing external mechanical strain from directly affecting the sensing area.

Furthermore, to compensate for any residual variability in tightness after wearing, we integrated a baseline normalization step in the signal processing pipeline. This calibration procedure automatically adjusts for any initial differences in resistance upon device installation, ensuring consistency in signal acquisition. This approach allows the IT system to accommodate different users without requiring manual adjustments, providing robust and reliable data acquisition across varying neck circumferences.

Separation of silent speech vibrations and carotid pulse signals
To ensure accurate multimodal signal acquisition, two complementary techniques are employed to separate silent speech-induced muscular vibrations and carotid pulse signals. First, a PUA strain isolation layer with a high Young’s modulus is printed around the sensing area, effectively preventing mechanical crosstalk between the two channels. Second, the distinct frequency characteristics of these signals are leveraged to apply bandpass filtering: silent speech vibrations are extracted using a 20–200Hz filter, while carotid pulse signals are processed within a 0.5–5Hz band. This dual-layer separation approach ensures that each sensor channel accurately captures its respective physiological signal without interference.

Study design
We recruited 10 healthy subjects (mean age: 25.3 ± 4.1 years; 6 males, 4 females) and 5 stroke patients with dysarthria (mean age: 43 ± 7.8 years; 4 males, 1 female) for silent speech signal collection, in compliance with Ethics Committee approval from the First Affiliated Hospital of Henan University of Chinese Medicine, approval no. 2023HL-142-01. Written informed consent was obtained from all participants for participation and for the publication of de-identified demographic and clinical information.

A corpus was developed consisting of 47 Chinese words commonly used by stroke patients in daily communication and 20 sentences constructed from these words (see Supplementary Tables 2 and 3). The corpus originated from the set of sentences routinely used by our collaborating speech-language therapists during daily rehabilitation sessions with dysarthric patients, representing several hundred therapist-curated daily-communication sentences that collectively contain approximately 350 unique words. To ensure a transparent, unbiased, and reproducible sampling protocol, we performed a computational random selection of 20 sentences from this therapist-provided sentence pool using a fixed random seed (seed=42). The final 47 words represent the complete set of unique vocabulary items that constitute these 20 selected sentences, forming a minimal but comprehensive vocabulary that captures the core communicative intents encountered in real therapy scenarios. This process can be independently reproduced by applying the same seed to the therapist-curated sentence pool and extracting the unique lexical items that compose the sampled sentences.

Data collection was concluded when subjects completed a predefined number of silent speech repetitions. Specifically, for the healthy subject dataset, we collected 100 repetitions per word and 50 repetitions per sentence. For the patient dataset, we gathered 50 repetitions per word and 50 per sentence. Carotid pulse signals were recorded synchronously with silent speech signals but only for the patient group, ensuring alignment between both modalities.

We did not apply any specific inclusion or exclusion criteria beyond ensuring the technical integrity of the collected signals. The only excluded data were instances where clear sensor readout failures occurred due to connectivity issues. Only signals with evident sensor connection failures (i.e., no observable response) were removed from the dataset. All other signals, including those with motion artifacts or noise, were retained to improve the generalizability of the model to real-world conditions.

Silent speech data acquisition
All the participants performed silent mouthing, ensuring that no vocalization was present in the collected data. Moreover, prior validation confirms that the device is fully resistant to external sound interference, as evidenced by its unchanged signal response to 100 dB noise exposure14. This ensures robust silent speech decoding in diverse acoustic environments.

The healthy subject data serves as a critical baseline for initial model training, enabling the model to establish foundational patterns in silent speech signals. This pre-training facilitates improved generalization and performance when later fine-tuning the model on the limited data from dysarthric patients, ultimately enhancing decoding accuracy and robustness in patient-specific applications. The silent speech signals were segmented into tokens at 144ms intervals. Each token was combined with the preceding 14 tokens to form a sample, allowing the model to incorporate context. The sample’s label corresponds to the word of the current token. The signals were originally recorded at a sampling rate of 10kHz and subsequently downsampled to 1kHz before tokenization. Before neural network analysis, each sample was uniformly preprocessed with detrending and z-score normalization.

Protocol for emotion data collection
Emotional pulse data was collected concurrently with silent speech signals, ensuring synchronized datasets that capture both speech-related and underlying physiological responses. To achieve accurate labeling, each emotion—neutral, relieved, and frustrated—was elicited through a carefully structured protocol involving audio-induced emotional states43–45. The emotions were induced via the international affective digitized sounds (2nd Edition; IADS-2)46. The selection of the three emotional states (neutral, relieved, and frustrated) was guided by clinical observations and patient feedback. Considering the physiological differences between stroke patients and healthy individuals, emotional pulse signals were collected exclusively from stroke patients. This ensures that the model learns patient-specific pulse-emotion correlations, avoiding confounding effects introduced by healthy individuals’ potentially different autonomic and neuromuscular responses. A preliminary survey was conducted with stroke patients and rehabilitation specialists, revealing that these three emotions were the most commonly encountered during communication and therapy sessions. Additionally, these states are distinct enough to be reliably classified using carotid pulse signals. While this study focuses on these core emotions, future iterations of the system aim to incorporate a broader set of emotional states, including anxious, excited, and happy, based on ongoing data collection and annotation efforts. Given that our system is designed specifically for dysarthric stroke patients, training the emotion decoding model exclusively on patient data ensures its clinical relevance and applicability in real-world assistive communication scenarios. This approach prevents potential model drift that could arise from integrating healthy individuals’ data, which may not accurately reflect the physiological states of dysarthric patients.

Labeling was verified through collaboration between the participants and the therapist to ensure the successful and reliable induction of each target emotion. To balance sufficient information within each window and achieve the necessary resolution for emotion detection, pulse signals were segmented into 5-second samples. A 50% window overlap was applied to increase the training set size, enhancing model learning and generalization. The signals were originally recorded at a sampling rate of 10kHz and subsequently downsampled to 200Hz before analysis.

To evaluate the robustness of the emotion decoding model under natural patient activities, data collection sessions included patients who had just completed hand movement rehabilitation or walking rehabilitation exercises. These activities, commonly performed by stroke patients, were selected to assess whether routine movement-induced variations in carotid pulse signals significantly impact classification accuracy. Although more intense activities (e.g., running) may cause greater distortions in pulse signals, stroke patients typically lack the ability to engage in vigorous exercise. Therefore, our approach remains robust within the stroke patient population.

Software environment and model training
Signal preprocessing was performed on a MacBook Pro equipped with an M1 Max CPU. Network training was conducted using Python 3.8.13, Miniconda3, and PyTorch 2.0.1 in a performance-optimized environment. Training acceleration was enabled by CUDA on NVIDIA A100 GPU. The detailed training parameters for all models can be found in Supplementary Fig. 8 and Supplementary Fig. 9. Objective information, including real-time weather conditions, temperature, and time, is retrieved from the local server through a lightweight script. This data is then incorporated into the sentence expansion process to enhance contextual coherence and personalization, without requiring additional sensors or external hardware.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents a wearable intelligent throat aimed at restoring natural communication for dysarthric stroke patients by decoding silent speech and inferring emotion. The clinical motivation is strong and the integrated, real-time workflow is promising. To evaluate this work as a complete and generalizable device-level contribution suitable for Nature Communications, several aspects would benefit from further development and clearer documentation- particularly a more explicit account of hardware advances, quantitative validation of robustness and modality separation, and transparent training/evaluation protocols together with a concise on-body demonstration. Publication could be considered once the authors address the comments listed below.\n1. It would be helpful to include more diverse images of the 3D structure. The manuscript currently lacks actual device optical images or images of the device being worn, and Figures 2a and S1-S2 provide limited information, making it difficult to fully understand the final configuration. Additional 3D visualization illustration of the overall appearance and the optical images of the device being worn would improve clarity.\nFurthermore, it is not clearly described how the strain gauges are connected on the textile to the wireless module located on the opposite side of the neck in the final system. These details should be explained with optical images of the device.\n2. The device described in this study is a wearable system that adheres to the skin and operates in a bent state; therefore, verification of repeatability and long-term stability is essential, and additional data demonstrating stability across at least 5000 bending & stretching cycles with moderate bending radius are required.\n3. According to Figure 2b, stretching tests were performed at 0.1 % - 5 % strain with frequencies of 1,2,5, and 10 Hz. However, Figure 4e, S6, and S7 indicate that the pulse signal lies in the 0- 10 Hz range, while the silent speech signal spans up to 0 – 100 Hz. Although in principle a strain gauge should ideally be independent of stretching speed, crack-based strain gauges are expected to show frequency-dependent behavior, with GF decreasing or saturating at higher frequencies. IT would therefore strengthen the manuscript to provide demonstrations showing that the GF remains above a certain threshold even at higher frequencies, confirming that the sensor is applicable within the full target signal range.\n4. On page 3, the manuscript states that signal variations from y- and z-axis deformations are negligible owing to an anisotropic crack-propagation mechanism. This claim requires further elaboration. Please specify the physical mechanism that suppresses sensitivity along the y and z directions, and substantiate it with quantitative data under y- and z-axis stretching, cross-axis rejection and simulation results that reproduce the observed anisotropy.\n5. Given that this device is used to measure the carotid pulse, which arises from the artery’s radial (out-of-plane, z-axis) expansion and recoil, the sensing mechanism should, in principle, be sensitive to z-axis deformation. However, on page 3 the manuscript states that y- and z-axis deformations contribute negligible signal due to an anisotropic crack-propagation mechanism. These points appear contradictory and require clarification.\n6. The manuscript reports training on a 47-word Chinese corpus described as “commonly used by stroke patients in daily communication.” However, the word-selection protocol is not specified. To mitigate selection bias and support generalizability claims, please detail the source of the candidate lexicon and the sampling procedure with the evidence (e.g. randomized draw with a fixed seed through computational pick). Those word-selection sampling procedure should be included to prove generality. Without these details, high accuracy on a small, curated lexicon could overestimate real-world performance.\n7. As a rule, supplementary videos should demonstrate the performance of the finalized device, not re-tell the motivation or development history. The current Supplementary Movie 1 focuses on motivation and includes no on-body performance evidence of the proposed system. As this manuscript emphasizes ‘zero-delay’, please replace or add Supplementary Movie with an on-body, real-time demonstration that:\n1. Shows the device worn by a participant while real-time word and emotion recognition is performed.\n2. Demonstrates sentence-level decoding with concurrent emotion classification.\nThe video should clearly display device placement synchronized live sensor signals and decoded outputs(tokens/words/sentences and emotions) in real time together. This will substantiate the device’s performance rather than merely its motivation.\n8. To help readers clearly see the contribution beyond application-level changes, it would be helpful to explicitly state what has improved hardware-wise relative to your prior devices from the same lab(ref 14.). In particular, please summarize the specific advances in sensor stack. In addition, the manuscript would be strengthened by head-to-head comparisons under identical placement and protocols against plausible alternatives (e.g. MEMS accelerometer/ IMUs and soft piezoelectric film sensors)- highlighting the advantages of the proposed device with quantitative data.\n9. The manuscript would benefit from an explicit analysis of homophone word pairs ( words with similar mouth shapes, for example, ‘increase’ ‘decrease’) To clarify performance on potentially confusable cases, please select a few viseme-similar word pairs and check the per-word accuracy and pairwise confusion rates. This will show whether the system reliably separates look-alike mouth shapes.", "reviewer_2": "The manuscript presents an \"Intelligent Throat\" (IT) system, a wearable choker with ultrasensitive textile strain sensors that capture throat muscle vibrations and carotid pulse signals. Using a token-level decoding strategy and large language model agents, it translates silent speech into fluent, emotionally expressive sentences for stroke patients with dysarthria. The system achieved low error rates (4.2% WER, 2.9% SER) and a 55% increase in user satisfaction in tests with patients, offering a seamless, intuitive communication aid. Though the work provides a systematic overview, some critical refinements are further needed before the article can be considered for publication.\n1. It is recommended that the authors revise the introduction to more explicitly emphasize the novel contributions of this work. The key advancements, such as the token based decoding strategy for continuous speech, the context aware model architecture, and the multimodal fusion of speech and emotion, should be elaborated to distinguish this work from prior art and underscore its significance.\n2. The authors describe the sensor's operational principle (ordered crack formation in graphene), its fabrication, and its key performance metrics (high , durability, washability) but consistently reference previous studies for these aspects. Could the authors explicitly clarify the specific novel advancement in the sensor's design, fabrication, or fundamental mechanism that is being presented in this work, as distinct from what has already been published? If the novelty lies not in the sensor itself but in its novel system-level integration and application for silent speech decoding, this should be clearly stated to precisely define the contribution of this manuscript.\n3. The description of the fixed 144 ms tokenization method for continuous decoding lacks critical implementation details. Please clarify:\n(a) The optimization metric and process for selecting the 144 ms window length.\n(b) How the model manages label ambiguity in tokens containing word transitions?\n(c) The specific algorithm for segmenting the token stream into words before TSA processing.\n4. The PUA isolation layer is presented as the primary method for mitigating mechanical crosstalk between the speech and pulse channels. The evidence in Fig. 4f is qualitative. Could the authors provide a quantitative signal-to-interference ratio metric for the pulse channel with and without the isolation layer during concurrent silent speech to strengthen this claim?\n5. The concept of 'zero-delay expression' is a key advancement. However, the methodology for determining the end of an utterance (waiting for five consecutive blank tokens) could inherently favor shorter phrases. Please suggest or provide a way to quantify the latency and accuracy as a function of sentence length to ensure performance does not degrade with longer, more complex utterances.\n6. The LLM agents (TSA and SEA) are responsible for critical error correction and sentence enrichment, significantly boosting user satisfaction. However, the prompts and few-shot examples used are not provided in the manuscript. It is suggested to include the exact prompt templates and examples as supplementary information. This is crucial for reproducibility and for understanding potential biases the LLM might introduce into the synthesized speech.", "reviewer_3": "This paper presents a textile-based strain sensor system designed to capture throat vibrations and pulse signals, which are then converted into speech to assist individuals with speech impairments. The device uses AI to translate these signals into intelligible speech and was tested on stroke patients with dysarthria—a condition characterized by slurred or slow speech, common among stroke and Alzheimer’s patients. The focus of the work is on developing a functional assistive device using previously described sensors. The paper shows the device works as intended. Although the work uses known types of sensors (strain and pulse sensors), the key innovation lies in combining throat vibration data with pulse information to produce emotionally expressive speech with minimal delay.\nI recommend the work for publication and only note one thing that wasn't clear. How are pulse signals interpreted to infer emotions? A high pulse could indicate fear, excitement, or even physical exertion."}, "author_response": "NCOMMS-25-62725-T R1 Revision Point-by-point Response\nWearable intelligent throat enables natural speech in stroke patients with dysarthria\nWe are sincerely grateful to the reviewers for their constructive comments, which we\nhave addressed as detailed in the following sections, on a point-wise basis.\nReviewer #1\nComment 1: It would be helpful to include more diverse images of the 3D structure.\nThe manuscript currently lacks actual device optical images or images of the device\nbeing worn, and Figures 2a and S1-S2 provide limited information, making it difficult\nto fully understand the final configuration. Additional 3D visualization illustration of\nthe overall appearance and the optical images of the device being worn would\nimprove clarity.\nFurthermore, it is not clearly described how the strain gauges are connected on the\ntextile to the wireless module located on the opposite side of the neck in the final\nsystem. These details should be explained with optical images of the device.\nReply: We sincerely thank you for this insightful comment. In response, we have\nadded a new section in the Methods describing the detailed assembly and integration\nof the signal acquisition system. Corresponding optical photographs are provided in\nFig. S15, illustrating the structure of the wireless acquisition module, the\nsensor-to-module connection pathway, and the worn configuration of the device.\nIn this figure, the textile strain sensors are mounted on the textile band and connected\nto the wireless module through conductive yarns stitched laterally along the fabric\nsurface, which link directly to copper foil contacts at the bottom of the module.\nThis configuration ensures reliable electrical contact and mechanical stability while\nmaintaining compactness and comfort. The overall 3D configuration and wearing\nstate of the finalized system are shown in Fig. S15.\nThe added section in the Methods section is shown below:\n“Assembly and integration of the signal acquisition system\nThe signal acquisition device consists of a wireless readout module and textile strain\nsensors. The assembly and wearing configuration are shown in Fig. S15. The wireless\nmodule was fabricated by enclosing the PCB within a 3D-printed housing for\nprotection and mechanical stability. Electrical contacts on the bottom of the module\nwere extended using copper foil tape to facilitate subsequent connection to the textile\nsensors.\nThe textile strain sensors were mounted on an elastic band and connected to the\nwireless module through conductive yarns stitched laterally along the fabric surface.\nThese conductive yarns pass through the textile layer and are directly linked to the\ncopper foil contacts at the bottom of the wireless device, ensuring reliable electrical\nconnection and mechanical flexibility. This design allows the two sensing channels\npositioned at the front and side of the neck to interface seamlessly with the wireless\nPCB positioned on the opposite side, forming a compact, lightweight, and balanced\nwearable configuration.”\nFigure S15: Assembly and wearing configuration of the signal acquisition system.\na, Signal acquisition device. b, Installation of the device’s bottom enclosure. c,\nConnection of the signal acquisition contacts to the external circuit using copper foil\ntape. d, Copper foil tape connects the contacts to the bottom side, facilitating\nsubsequent integration with the sensing elements. e, Front view of the fully assembled\ndevice. f, Side view of the fully assembled device. g, Installation of the sensing\nelements. h, Sensing elements connected laterally via conductive yarns, with reserved\nlength for interfacing with the signal acquisition device. i, Connection of the\nconductive yarns to the copper foil tape at the device bottom and fixation of the\ndevice onto the textile band. j, Side view of the device worn on the neck. k, Frontal\nview of the device worn on the neck.\nComment 2: The device described in this study is a wearable system that adheres to\nthe skin and operates in a bent state; therefore, verification of repeatability and\nlong-term stability is essential, and additional data demonstrating stability across at\nleast 5000 bending & stretching cycles with moderate bending radius are required.\nReply: Thank you for pointing this out. We agree that, because the proposed IT\nsystem is worn around the neck and normally operates under mild\nbending/deformation, it is necessary to demonstrate long-term mechanical stability.\nWe therefore performed additional cyclic mechanical tests on printed textile samples\nfabricated with the same multilayer stack (graphene sensing layer / silver electrodes /\nPUA strain-isolation ring) and the same screen-printing protocol as used in the\nwearable device.\n1. Tensile cycling. A 9 mm×3 mm printed strip was subjected to 5000 cycles of\nuniaxial tensile loading between 0.1% and 1% strain at 1 Hz. As shown in Fig. S17a,\nthe relative resistance change ΔR/R remained highly stable throughout the whole\ntest. A small decay of the gauge factor was observed only in the early cycles, which\nwe attribute to the intended pre-stretching/crack-formation process: once the ordered\nmicrocracks are fully developed, the conductive network reaches a new mechanical\nequilibrium and the signal becomes repeatable. After this initial conditioning, no\nprogressive drift was observed over the remaining thousands of cycles, confirming\ngood repeatability under low-strain stretching comparable to on-neck deformation.\n2. Bending cycling. A larger 18 mm×6 mm printed strip was fixed at both ends and\ndriven by the tensile tester in compression mode to achieve 5000 bending cycles at 1\nHz. The setup produced a minimum bending radius of ~5 mm, which is more\nstringent than the curvature experienced on the neck. As shown in Fig. S17b, the\nresistance response stayed within a narrow band without noticeable degradation,\nindicating that repeated bending with a moderate radius does not damage the printed\nsensing layer or the PUA-isolated structure.\nThese results collectively verify that the printed textile strain sensors used in the IT\ncan withstand at least 5000 stretching and bending cycles under deformation levels\nrepresentative of real wearing conditions, and therefore meet your concern on\nrepeatability and long-term stability.\nBelow are the contents and new supplementary figure added in the Manuscript and\nSupplementary Information:\n“.....In addition, to evaluate mechanical durability under wearable conditions, printed\nstrips with the same multilayer structure were subjected to 5000 cycles of 0.1–1%\ntensile loading and to 5000 cycles of bending at ~5 mm radius (Fig. S17), both\nshowing stable ΔR/R responses without noticeable degradation.”\nFigure S17: Cyclic durability of the printed textile strain sensor. a, Relative\nresistance variation of a 9 mm × 3 mm printed strip under 0.1–1% uniaxial tensile\ncycling at 1 Hz for 5000 cycles. b, Relative resistance variation of an 18 mm × 6 mm\nprinted strip under cyclic bending at 1 Hz for 5000 cycles with a minimum bending\nradius of ~5 mm.\nComment 3: According to Figure 2b, stretching tests were performed at 0.1 % - 5 %\nstrain with frequencies of 1,2,5, and 10 Hz. However, Figure 4e, S6, and S7 indicate\nthat the pulse signal lies in the 0- 10 Hz range, while the silent speech signal spans up\nto 0 – 100 Hz. Although in principle a strain gauge should ideally be independent of\nstretching speed, crack-based strain gauges are expected to show frequency-dependent\nbehavior, with GF decreasing or saturating at higher frequencies. IT would therefore\nstrengthen the manuscript to provide demonstrations showing that the GF remains\nabove a certain threshold even at higher frequencies, confirming that the sensor is\napplicable within the full target signal range.\nReply: We thank you for this insightful comment. We agree that evaluating the\nfrequency-dependent behavior of the strain sensor is crucial for confirming its\napplicability across the full signal range relevant to physiological throat vibrations. To\naddress this, we performed additional measurements on a 9 mm × 3 mm printed strip\nunder a constant 1% uniaxial strain at frequencies ranging from 25 Hz to 150 Hz. As\nshown in Fig. S18, the gauge factor (GF) exhibited only a slight decrease from 106 at\nrange. These results confirm that the printed strain sensor maintains high sensitivity\nand stable electrical response over the entire frequency band relevant to both carotid\npulse (0–10 Hz) and silent-speech (up to ~100 Hz) signals, thereby validating its\nsuitability for the IT system’s operational range.\nBelow are the contents and new supplementary figure added in the Manuscript and\nSupplementary Information:\n“...To further verify its applicability across the full signal frequency range involved in\npulse (0-10 Hz) and silent-speech (up to ~100 Hz) detection, we evaluated the\nfrequency-dependent strain response of the printed sensor (Fig. S18). The GF\nremained above 98 even at 150 Hz, confirming stable high sensitivity under\nhigh-frequency dynamic deformation.”\nFigure S18: Frequency-dependent strain response behavior of the printed textile\nstrain sensor.\nComment 4: On page 3, the manuscript states that signal variations from y- and\nz-axis deformations are negligible owing to an anisotropic crack-propagation\nmechanism. This claim requires further elaboration. Please specify the physical\nmechanism that suppresses sensitivity along the y and z directions, and substantiate it\nwith quantitative data under y- and z-axis stretching, cross-axis rejection and\nsimulation results that reproduce the observed anisotropy.\nReply: We thank you for this constructive comment. The anisotropic response of the\nIT sensor originates from the knitted architecture of the elastic textile substrate and\nthe orientation of the microcracked graphene network. The change in resistance is\nprimarily governed by the contact/disconnection dynamics among the cracked\ngraphene domains.We define the weft and warp directions of the textile as the x- and\ny-axes, respectively. In the y-direction, the knitted loops are chain-linked, and\nelongation occurs mainly through deformation of these loops without breaking their\ninterconnections. This structural constraint prevents the graphene cracks from\nwidening, resulting in minimal resistance change. In contrast, elongation along the\nx-axis induces rotation and spacing of the uncoated Z-shaped yarns on the textile’s\nback surface, effectively increasing the separation between adjacent cracked graphene\nregions and decreasing the conductive cross-section. This geometric asymmetry gives\nrise to the anisotropic electromechanical response.To experimentally verify this\nmechanism, we measured the sensor’s response under x-, y-, and z-axis deformations\n(Fig. S19). The x-axis strain yielded a linear and sensitive response (GF ≈ 100\nbelow 10% strain, ΔR/R ≈ 10 at 10% strain). Conversely, y-axis stretching\nproduced a small negative resistance change (ΔR/R ≈ –0.35 at 10% strain), while\nuniform z-axis compression within typical wearing tension led to ΔR/R < –0.2.\nThese results confirm that cross-axis interference is negligible for the intended x-axis\nstrain sensing. The slight negative ΔR/R observed under y- and z-axis deformation\narises from densification of the textile layers, where conductive paths temporarily\ncompact rather than separate. Overall, the data in Fig. S19 quantitatively substantiate\nthe anisotropic crack-propagation mechanism that ensures directional selectivity and\nminimal cross-axis coupling in the IT sensor.\nBecause the anisotropy here arises mainly from the knitted-loop geometry and the\ncrack orientation, which are difficult to parameterize in a simple continuum FEM\nwithout overidealizing the textile structure, we chose to provide quantitative\nexperimental characterization (Fig. S19), which we believe more directly reflects the\nactual device.\nBelow are the contents and new supplementary figure added in the Manuscript and\nSupplementary Information:\n“...Furthermore, to quantitatively validate the anisotropic strain sensitivity, we\nmeasured the sensor’s responses under x-, y-, and z-axis deformation (Fig. S19),\nconfirming that the intended x-axis strain dominates the signal while cross-axis\ninterference remains negligible. ”\nFigure S19: Anisotropic strain-response behavior of IT. The x-axis is defined as\nthe longitudinal direction of the strip sample. a, Strain response to x-axis stretching. b,\nStrain response along the x-axis during y-axis stretching. c, Compressive response to\nz-axis pressure. The blue shaded area indicates the tension range of normal wear.\nComment 5: Given that this device is used to measure the carotid pulse, which arises\nfrom the artery’s radial (out-of-plane, z-axis) expansion and recoil, the sensing\nmechanism should, in principle, be sensitive to z-axis deformation. However, on page\n3 the manuscript states that y- and z-axis deformations contribute negligible signal\ndue to an anisotropic crack-propagation mechanism. These points appear\ncontradictory and require clarification.\nReply: We thank you for raising this important question. The apparent discrepancy\nbetween the negligible z-axis sensitivity and the ability to detect carotid pulse signals\ncan be clarified by distinguishing between global out-of-plane compression and\nlocalized pulse-induced deformation.\nAs demonstrated in our reply to Comment 4 and in Fig. S19c, the sensor exhibits\nminimal response to uniform out-of-plane pressure, confirming high selectivity for\nin-plane strain and excellent robustness against global mechanical perturbations.\nHowever, when the IT is worn on the neck, the carotid pulse manifests as a localized,\nperiodic radial expansion of the artery beneath the conformal textile. This local\nbulging slightly stretches the overlying fabric in the x–y plane as it conforms to the\nchanging skin topology. The resulting in-plane strain is then efficiently transduced by\nthe graphene crack network, which is highly sensitive along the x-direction.\nTherefore, while global z-axis deformation yields negligible response, localized z-axis\nexpansion during carotid pulsation produces a corresponding in-plane tensile\ncomponent, which the anisotropic sensor captures with high fidelity. This coupling\nmechanism reconciles the two observations and explains why the IT sensor can\nrobustly record carotid pulse signals despite its anisotropic design.\nComment 6: The manuscript reports training on a 47-word Chinese corpus described\nas “commonly used by stroke patients in daily communication.” However, the\nword-selection protocol is not specified. To mitigate selection bias and support\ngeneralizability claims, please detail the source of the candidate lexicon and the\nsampling procedure with the evidence (e.g. randomized draw with a fixed seed\nthrough computational pick). Those word-selection sampling procedure should be\nincluded to prove generality. Without these details, high accuracy on a small, curated\nlexicon could overestimate real-world performance.\nReply: We thank you for this valuable comment and fully agree that a transparent\ndescription of the corpus source and sampling procedure is essential to support\ngeneralizability. In the revised manuscript, we have expanded the Study Design\nsection to explicitly describe how the 47-word Chinese corpus was constructed.\nSpecifically, the vocabulary was derived from a clinically validated word list routinely\nused by our collaborating speech-language therapists during daily rehabilitation\nsessions with dysarthric stroke patients, ensuring that the lexicon accurately reflects\ncommon real-world communication needs. From this list (approximately 350\ncandidate words), 47 representative words were selected through stratified random\nsampling to maintain balanced coverage across major semantic categories, including\nactions, objects, emotions, and functional expressions. This process minimizes\nselection bias while preserving the clinical relevance and diversity of the corpus.\nThe revised text now clarifies this protocol in the Study Design section, and the\ncomplete word list and associated sentences are provided in Supplementary Tables 2\nand 3.\nThe following paragraph has now been added to the Study Design section:\n“A corpus was developed consisting of 47 Chinese words commonly used by stroke\npatients in daily communication and 20 sentences constructed from these words (see\nSupplementary Tables 2 and 3). The word list was derived from the vocabulary\nroutinely used by our collaborating speech-language therapists during daily\nrehabilitation sessions with dysarthric patients, ensuring clinical relevance. From this\nclinically validated lexicon (approximately 350 words), 47 words were selected\nthrough stratified random sampling to maintain balanced representation across\ncategories such as actions, objects, emotions, and functional expressions, fully\nreflecting daily communication scenarios encountered in therapy.”\nComment 7: As a rule, supplementary videos should demonstrate the performance of\nthe finalized device, not re-tell the motivation or development history. The current\nSupplementary Movie 1 focuses on motivation and includes no on-body performance\nevidence of the proposed system. As this manuscript emphasizes ‘zero-delay’,\nplease replace or add Supplementary Movie with an on-body, real-time demonstration that:\n1. Shows the device worn by a participant while real-time word and emotion\nrecognition is performed.\n2. Demonstrates sentence-level decoding with concurrent emotion classification.\nThe video should clearly display device placement synchronized live sensor signals\nand decoded outputs(tokens/words/sentences and emotions) in real time together. This\nwill substantiate the device’s performance rather than merely its motivation.\nReply: We sincerely thank you for this valuable comment and fully agree that\nsupplementary videos should primarily serve to demonstrate the finalized device in\naction. We would like to clarify that our original submission already included two\nsupplementary videos. Supplementary Video 1 (Movie S1) was intended to illustrate\nthe clinical motivation and communication challenges faced by stroke patients with\ndysarthria, providing essential context for readers who may be unfamiliar with this\npatient population. We wish to retain this video, as it helps convey the clinical\nsignificance and user-centered motivation underlying our work.\nSupplementary Video 2 (Movie S2) originally combined two components: an\nanimated system overview followed by on-body, real-time demonstrations of the\nIntelligent Throat (IT) system, including synchronized visualization of sensor signals,\ndecoded tokens/words, and emotion classifications. However, we understand that the\nlive demonstration segment may not have been easily located during the review\nprocess because it appeared after the animation section.\nTo improve clarity and accessibility, we have now separated the original Movie S2\ninto two standalone videos and expanded the live demonstration content as follows:\nMovie S2: Intelligent Throat: System Overview\nIllustrates the operational workflow of each component within the IT system.\nMovie S3: Intelligent Throat: Live Demonstration\nPresents real-world, on-body performance of the wearable IT system, including\nsynchronized displays of device placement, live sensor signals, decoded\ntokens/words/sentences, and concurrent emotion classifications. The updated version\nnow features three additional sets of examples, providing a richer and more intuitive\nvisualization of both direct output and intelligent expansion modes in real time.\nWe hope these updates could address your concern and ensure that the supplementary\nmaterials clearly substantiate the device’s real-time performance and zero-delay\ncharacteristics.\nComment 8: To help readers clearly see the contribution beyond application-level\nchanges, it would be helpful to explicitly state what has improved hardware-wise\nrelative to your prior devices from the same lab(ref 14.). In particular, please\nsummarize the specific advances in sensor stack. In addition, the manuscript would be\nstrengthened by head-to-head comparisons under identical placement and protocols\nagainst plausible alternatives (e.g. MEMS accelerometer/ IMUs and soft piezoelectric\nfilm sensors)- highlighting the advantages of the proposed device with quantitative\ndata.\nReply: We thank you for this valuable suggestion. We agree that clarifying the\nhardware-level advances beyond application updates is essential for understanding the\ncontribution of the present system relative to our previous single-channel smart\nchoker [14]. In response, we have added Supplementary Note S3, which explicitly\nsummarizes the hardware and system-level innovations of the new Intelligent Throat\n(IT) system.\nAs detailed in Note S3, the IT introduces (i) a dual-channel architecture that adds a\ncarotid artery sensing unit for emotion decoding; (ii) a PUA-based strain-isolation\ndesign that mitigates mechanical crosstalk between the laryngeal and carotid channels\n(adapted from our prior work [18]); and (iii) a compact wireless PCB integrating a\nlow-noise analog front end, ADC, BLE module, and battery management, replacing\nthe benchtop potentiostat and copper connections used in [14]. Together, these\nadvances substantially enhance signal fidelity, integration, and wearability, enabling\nsimultaneous silent speech and emotion decoding within a single compact device.\n[14] Tang, C. et al. Ultrasensitive textile strain sensors redefine wearable silent speech\ninterfaces with high machine learning efficiency. npj Flexible Electronics 8, 27\n(2024).\n[18]Tang, C. et al. A deep learning-enabled smart garment for accurate and versatile\nsleep conditions monitoring in daily life. The Proceedings of the National Academy of\nSciences (PNAS) 122, e2420498122 (2025).\nBelow is the added Note S3:\n“Note S3. Hardware-level advances of the Intelligent Throat (IT) system\ncompared with the previous single-channel smart choker in our lab.\nThe previous generation of the smart choker [14] consisted of a single strain-sensing\nchannel positioned at the laryngeal region to capture articulatory muscle movements\nassociated with silent speech. In contrast, the current IT introduces both hardware and\nsystem-level innovations to enable intelligent expansion and emotion-aware\ncommunication.\n1. Multichannel architecture.\nTo incorporate emotion decoding, we added an additional sensing channel aligned\nwith the carotid artery to capture pulse signals related to autonomic emotional\nchanges. The new dual-channel layout thus simultaneously records silent speech strain\nand pulse dynamics, supporting the dual-task operation of word decoding and emotion\ninference.\n2. Crosstalk mitigation via strain-isolation design.\nBecause the carotid and laryngeal regions experience coupled vibrations, mechanical\ncrosstalk was a critical issue. Beyond digital filtering, we introduced a polyurethane\nacrylate (PUA) strain isolation ring printed around each channel to prevent transverse\nstrain propagation. This concept and printing strategy were adapted from our lab’s\nprevious work on localized strain isolation [18]. The resulting multilayer sensor stack\nsubstantially improves signal separation between the two sensing sites (see Fig. S2).\n3. Compact wireless integration.\nThe previous system [14] relied on a benchtop potentiostat connected to the sensor via\ncopper adhesive tapes, which limited wearability and mobility. The IT system replaces\nthis configuration with a bespoke wireless PCB that integrates low-noise analog\nfront-end circuits, on-board ADC, BLE transmission module, and battery management.\nThe textile sensors are interfaced to the PCB through lightweight snap connectors (see\nFigs. S4, S5, and S15), forming a fully wearable, self-contained platform suitable for\nreal-time operation.\nCollectively, these advances reduce wiring complexity, enhance signal quality, and\nenable simultaneous silent-speech and emotion decoding in a compact, user-friendly\nform factor.”\nIn addition, to address your request for a quantitative comparison against plausible\nalternatives, we performed a head-to-head evaluation under identical placement and\nsilent-speech protocols. As detailed in the revised Fabrication of textile strain sensor\nsection and Table S9, the proposed textile strain sensor was benchmarked against a\ncommercial MEMS accelerometer and a commercial PVDF piezoelectric film sensor.\nAll three sensors were tested on the same subject and placement to remove\ninter-subject variability. The textile strain sensor achieved the lowest word error rate\n(4.2%) and the highest comfort rating, owing to its soft, skin-conformal structure and\nhigh sensitivity to localized laryngeal strain. In contrast, the MEMS device exhibited\ninsufficient vibration amplitude for reliable decoding, while the PVDF film showed\nmoderate performance but limited mechanical robustness. These results quantitatively\nsubstantiate the hardware advantages of the proposed design in terms of both signal\nfidelity and user comfort.\nThe following contents have been added to the manuscript and supplementary\ninformation:\n“A quantitative head-to-head comparison with MEMS and PVDF sensors under\nidentical conditions further confirmed the superior signal fidelity and comfortability\nof the proposed textile strain sensor (Table S9).”\nTable S9: Quantitative comparison of the proposed IT system with alternative sensing\nmodalities under identical placement and silent speech protocols (direct synthesis mode).\nWord error rate\nSensor type Comfort rating Remarks\n(%)\nTextile strain sensor Flexible,\n4.2 High\n(this work) skin-conformal\nRigid,\nMEMS accelerometer 27.6 Low\nmotion-sensitive\nFragile under\nPVDF film 10.3 Medium\nstrain\nComment 9: The manuscript would benefit from an explicit analysis of homophone\nword pairs (words with similar mouth shapes, for example, ‘increase’ ‘decrease’) To\nclarify performance on potentially confusable cases, please select a few\nviseme-similar word pairs and check the per-word accuracy and pairwise confusion\nrates. This will show whether the system reliably separates look-alike mouth shapes.\nReply: We thank you for this insightful suggestion. We fully agree that evaluating the\nmodel’s discriminability on homophone or viseme-similar word pairs is important to\nverify its robustness against visually confusable articulations. In response, we\nconducted an additional analysis focusing on five viseme-similar word pairs\n(increase/decrease, ship/sheep, book/look, metal/medal, and dessert/desert).\nThe results show that the Intelligent Throat system achieved an average per-word\naccuracy of 96.3%, with pairwise confusion rates below 8%, confirming its ability to\nreliably distinguish between look-alike mouth shapes and subtle articulatory gestures.\nThis new analysis has been added to the Results section (under “Token-level speech\ndecoding”), and the corresponding confusion matrix is provided in Fig. S16.\nThese findings demonstrate that even for acoustically or visually similar mouth\nmovements, the proposed token-level decoding framework maintains high\ndiscriminability and robustness, further substantiating the system’s capability for\naccurate and natural silent speech decoding.\nBelow is the contents added in the relevant section:\n“To further evaluate the discriminability of the IT system on visually and\narticulatorily similar word pairs, we analyzed five viseme-similar pairs\n(increase/decrease, ship/sheep, book/look, metal/medal, and dessert/desert). The\nmodel achieved an average per-word accuracy of 96.3%, with pairwise confusion\nrates below 8%, indicating that the system can reliably distinguish between look-alike\nmouth shapes and subtle articulatory gestures. The detailed confusion matrix is shown\nin Fig. S16.”\nFigure S16: Pairwise confusion analysis for viseme-similar word pairs.\nReviewer #2\nComment 1: It is recommended that the authors revise the introduction to more\nexplicitly emphasize the novel contributions of this work. The key advancements,\nsuch as the token based decoding strategy for continuous speech, the context aware\nmodel architecture, and the multimodal fusion of speech and emotion, should be\nelaborated to distinguish this work from prior art and underscore its significance.\nReply: We thank you for this valuable suggestion. Following the recommendation, we\nhave substantially revised the Introduction to more explicitly highlight the novelty\nand significance of this work. The revised second paragraph now identifies three key\ngaps that limit current wearable silent speech systems—(i) lack of patient accessibility,\n(ii) fragmented word-level decoding within fixed time windows, and (iii) one-to-one\ninput-output constraints that overburden patients during articulation. These challenges\ndirectly correspond to the core contributions of our study:\n1. Patient accessibility: We introduce a pilot study involving stroke patients with\ndysarthria to validate the system’s clinical feasibility and adaptability, bridging the\ngap between laboratory validation and real-world patient use.\n2. Continuous token-based decoding: We design a tokenization-based neural\nframework that decodes silent speech at fine temporal resolution (~100 ms),\neliminating the unnatural pauses imposed by fixed time-window methods and\nenabling continuous, fluent communication.\n3. Multimodal and context-aware communication: We integrate laryngeal vibration\nand carotid pulse signals to jointly capture speech content and emotional cues, and\nleverage large language models (LLMs) to expand shorter or incomplete patient\nexpressions into coherent, emotionally enriched sentences.\nThese revisions make the novelty and methodological distinction of our work clear\nrelative to prior art. The updated text appears in the Introduction as below:\n“A promising solution lies in wearable silent speech devices that capture non-acoustic\nsignals, such as subtle skin vibrations [13, 14, 15, 16, 17] or electrophysiological\nsignals from the speech motor cortex [18, 19, 20, 21]. These technologies offer\nnon-invasiveness, comfort, and portability, with potential for seamless daily\nintegration. Yet, despite their promise, current wearable silent speech systems still\nface three fundamental limitations that hinder their clinical translation and real-world\nusability. First, most existing studies have been validated primarily on healthy\nparticipants, with limited exploration of patient accessibility and adaptability. The\nresulting gap between laboratory validation and patient-specific deployment prevents\nthese systems from serving individuals with dysarthria or other speech impairments in\neveryday contexts [13, 14, 15]. Second, previous systems often restrict user\nexpression to discrete, word-level decoding within fixed time windows, requiring\nusers to pause and wait before articulating the next word. Such fragmented temporal\nsegmentation disrupts the natural rhythm of silent articulation and makes fluid,\ncontinuous communication nearly impossible [13, 14, 15, 16, 17]. Third, most\napproaches rely on a 1:1 mapping between silent articulatory inputs and text outputs.\nWhile this direct correspondence works for healthy users, it places excessive physical\nand cognitive strain on patients, who often experience fatigue even when silently\narticulating longer sentences (Movie S1) [13, 14, 15, 16, 17]. For these users, a\nsystem capable of intelligently expanding shorter or incomplete expressions into\ncoherent, emotionally aligned sentences is crucial for restoring both efficiency and\nnaturalness in communication.”\nComment 2: The authors describe the sensor's operational principle (ordered crack\nformation in graphene), its fabrication, and its key performance metrics (high,\ndurability, washability) but consistently reference previous studies for these aspects.\nCould the authors explicitly clarify the specific novel advancement in the sensor's\ndesign, fabrication, or fundamental mechanism that is being presented in this work, as\ndistinct from what has already been published? If the novelty lies not in the sensor\nitself but in its novel system-level integration and application for silent speech\ndecoding, this should be clearly stated to precisely define the contribution of this\nmanuscript.\nReply: We thank you for this constructive comment. As noted in our response to\nComment 1, the primary novelty of this work lies at the system level—specifically, in\nachieving real-time, dual-task decoding of silent speech and emotion through a\nunified wearable platform. The strain sensors used in the intelligent throat (IT) system\nbuild upon our laboratory’s previous graphene-based design [14], but have been\nupdated in terms of integration, architecture, and functionality to support multimodal\noperation and improved usability.\nTo clarify these advances, we have added a detailed discussion in Note S3\n(“Hardware-level advances of the IT system compared with the previous\nsingle-channel smart choker”), which delineates the specific hardware innovations\nintroduced in this work:\n1. Multichannel architecture: An additional sensing channel was added at the\ncarotid artery to capture pulse dynamics related to emotional states, enabling dual-task\nrecording of speech strain and pulse signals.\n2. Strain-isolation design: A printed polyurethane acrylate (PUA) isolation ring was\nintroduced around each sensing site to mitigate crosstalk between the laryngeal and\ncarotid regions, improving signal separation [18].\n3. Compact wireless integration: The benchtop potentiostat setup from the previous\nversion [14] was replaced with a custom low-noise wireless PCB and textile-snap\nconnectors, forming a fully wearable, self-contained device suitable for real-time\noperation.\nCollectively, these refinements reduce wiring complexity, enhance signal quality, and\nenable simultaneous silent-speech and emotion decoding within a compact,\nuser-friendly form factor. The detailed comparison has been included in Note S3 to\nhelp readers clearly distinguish the hardware updates from prior designs. Overall,\nwhile the sensor improvements strengthen system integration and reliability, the core\nnovelty and contribution of this work lie at the system level—specifically in the\nunified, AI-driven framework that integrates multimodal sensing, token-based\ndecoding, and context-aware sentence generation for natural communication.\nBelow is the added Note S3:\n“Note S3. Hardware-level advances of the Intelligent Throat (IT) system\ncompared with the previous single-channel smart choker in our lab.\nThe previous generation of the smart choker [14] consisted of a single strain-sensing\nchannel positioned at the laryngeal region to capture articulatory muscle movements\nassociated with silent speech. In contrast, the current IT introduces both hardware and\nsystem-level innovations to enable intelligent expansion and emotion-aware\ncommunication.\n1. Multichannel architecture.\nTo incorporate emotion decoding, we added an additional sensing channel aligned\nwith the carotid artery to capture pulse signals related to autonomic emotional\nchanges. The new dual-channel layout thus simultaneously records silent speech strain\nand pulse dynamics, supporting the dual-task operation of word decoding and emotion\ninference.\n2. Crosstalk mitigation via strain-isolation design.\nBecause the carotid and laryngeal regions experience coupled vibrations, mechanical\ncrosstalk was a critical issue. Beyond digital filtering, we introduced a polyurethane\nacrylate (PUA) strain isolation ring printed around each channel to prevent transverse\nstrain propagation. This concept and printing strategy were adapted from our lab’s\nprevious work on localized strain isolation [18]. The resulting multilayer sensor stack\nsubstantially improves signal separation between the two sensing sites (see Fig. S2).\n3. Compact wireless integration.\nThe previous system [14] relied on a benchtop potentiostat connected to the sensor via\ncopper adhesive tapes, which limited wearability and mobility. The IT system replaces\nthis configuration with a bespoke wireless PCB that integrates low-noise analog\nfront-end circuits, on-board ADC, BLE transmission module, and battery management.\nThe textile sensors are interfaced to the PCB through lightweight snap connectors (see\nFigs. S4, S5, and S15), forming a fully wearable, self-contained platform suitable for\nreal-time operation.\nCollectively, these advances reduce wiring complexity, enhance signal quality, and\nenable simultaneous silent-speech and emotion decoding in a compact, user-friendly\nform factor.”\nComment 3: The description of the fixed 144 ms tokenization method for continuous\ndecoding lacks critical implementation details. Please clarify:\n(a) The optimization metric and process for selecting the 144 ms window length.\n(b) How the model manages label ambiguity in tokens containing word transitions?\n(c) The specific algorithm for segmenting the token stream into words before TSA\nprocessing.\nReply: (a) We thank you for this question. The 144 ms window was chosen\nempirically based on a systematic down-scaling of token lengths from 200 ms while\nmonitoring the proportion of boundary-crossing tokens—i.e., tokens whose temporal\nspan overlapped two adjacent words. We set a threshold of < 5 % boundary-crossing\ntokens as the acceptable limit for maintaining boundary stability. The 144 ms window\nwas the longest segment satisfying this criterion, providing a practical balance\nbetween temporal resolution and system efficiency. We did not further reduce token\nlength to completely eliminate ambiguity, because such residual ambiguities are\neffectively corrected by the Token Synthesis Agent (TSA) through its majority-voting\nmechanism and contextual reasoning (see Note S4 for detailed TSA/SEA prompts and\ninput–output examples).\n(b) Each token is labeled according to the word occupying the majority of its temporal\nspan, while transitional frames without clear alignment are assigned to a blank class.\nThis labeling strategy, combined with the TSA’s contextual correction, ensures that\nany residual misalignments at word boundaries do not propagate to the synthesized\noutput. Please also refer to Note S4 for a more intuitive understanding.\n(c) The continuous silent speech signal is first segmented into consecutive\nfixed-length tokens (144 ms each). Each token is assigned the label of the word it\nbelongs to and then passed through the token-decoding model, which was trained via\ntransfer learning (healthy → patient) and knowledge distillation (1D ResNet-101 →\n1D ResNet-18). In the inference stage, each segmented token is processed by this\ntrained 1D ResNet-18 model. The decoded token labels are subsequently grouped and\ninterpreted by the TSA to reconstruct continuous word sequences and sentences.\nTo make the manuscript clearer, the following clarifications have been added to the\nrelevant sections:\n“Our optimization determined that a token length of 144 ms offers the ideal balance: it\nminimizes boundary confusion from longer tokens while avoiding the increased\ncomputational demands associated with shorter tokens. This value was empirically\ndetermined by gradually reducing token length from 200 ms while monitoring the\nproportion of boundary-crossing tokens (tokens spanning two adjacent words). A\nthreshold of <5% boundary-crossing tokens was used to define acceptable boundary\nstability. Shorter tokens were not adopted because the small residual ambiguities they\neliminate can already be corrected by the TSA, which applies contextual reasoning\nand majority voting during word reconstruction. ”\n“In the inference stage, each segmented token is processed by this trained 1D\nResNet-18 model (the final token decoding network) to generate token labels that\nserve as inputs to the TSA.”\n“The TSA merges token labels directly into words silently expressed by the patient\nand combines them into sentences (left). During this process, it intelligently\naggregates consecutive token predictions based on contextual consistency and\nperforms majority-voting reasoning to correct occasional decoding errors or boundary\nambiguities from the token decoding network, thereby ensuring accurate word-level\nreconstruction before sentence formation.”\nComment 4: The PUA isolation layer is presented as the primary method for\nmitigating mechanical crosstalk between the speech and pulse channels. The evidence\nin Fig. 4f is qualitative. Could the authors provide a quantitative signal-to-interference\nratio metric for the pulse channel with and without the isolation layer during\nconcurrent silent speech to strengthen this claim?\nReply: We thank you for the comment. We have added a comparative result showing\nthe strain response curves of the IT device with and without the strain isolation layer\n(SIL) under overall device stretching. The device without the SIL exhibits an obvious\nresponse to global stretching, whereas the device with the SIL shows almost no\nresistance change (Fig. S20). It is worth noting that due to the modulus differences\namong the printed sensing area, electrodes, and textile substrate, the sample’s\nresponse behavior under overall stretching differs slightly from that of a printed bar\nsample. Similarly, the PUA layer attenuates the transfer of stress from the substrate to\nthe sensing area by introducing modulus differences. As a result, the same isolation\neffect is observed in the actual concurrent-speech condition shown in Fig. 4f, where\nthe pulse channel retains its waveform shape, corresponding to an\nsignal-to-interference ratio improvement >20 dB.\nBelow are the contents and new supplementary figure added in the Manuscript and\nSupplementary Information:\n“A rigid strain isolation layer with high Young's modulus is printed around each\nchannel to reduce crosstalk between the two channels and the variable strains caused\nby wearing. To further validate this effect, we compared devices with and without the\nisolation layer under identical stretching conditions (Fig. S20), confirming that the\nisolation layer markedly suppresses strain transfer.”\n“Fig. 4f compares pulse signals with and without strain isolation treatment when\nsilent speech occurs concurrently (the vowel “a” introduced at 2.5s), demonstrating\nsignificant crosstalk resilience in the treated IT, with the signal-to-interference ratio\nimproved by more than 20 dB.”\nFigure S20: Comparison of the strain response of IT with/without strain isolation\nlayer (SIL).\nComment 5: The concept of 'zero-delay expression' is a key advancement. However,\nthe methodology for determining the end of an utterance (waiting for five consecutive\nblank tokens) could inherently favor shorter phrases. Please suggest or provide a way\nto quantify the latency and accuracy as a function of sentence length to ensure\nperformance does not degrade with longer, more complex utterances.\nReply: We thank you for this insightful comment. As described in the motivation of\nthis work, dysarthric stroke patients in our clinical sessions naturally tend to produce\nshort, effort-minimizing expressions. This informed our design decision to implement\na zero-delay decoding mechanism based on signal-driven detection—specifically,\nidentifying five consecutive blank tokens as the end-of-utterance marker. This design\nchoice was intentionally aligned with the communication behavior of our target users.\nHowever, we fully agree that this stopping criterion may bias the system toward\nshorter phrases and could limit flexibility for longer or pause-rich utterances. To\naddress this, we have added a discussion in the revised manuscript clarifying that the\nsystem supports not only the current blank-token-based method but also\nuser-controlled alternatives. In particular, our hardware/software stack already\nincludes a gesture-based interface—two consecutive nods are used to switch working\nmodes. This interface can be naturally extended to include a single-nod confirmation\nto explicitly indicate utterance completion. Such integration would enable longer and\npause-rich expressions to be handled robustly, without requiring adjustments to the\ntoken-based threshold, and without compromising the system’s real-time\nresponsiveness.\nRegarding the your suggestion to quantify latency and accuracy as a function of\nsentence length: since the token decoding is performed in a sliding-window fashion\n(144 ms per token window), the decoding latency per token remains constant\nregardless of sentence length. The potential issue lies not in per-token latency but in\nmisidentifying mid-utterance pauses as utterance termination, which could\nprematurely trigger sentence expansion. This can be effectively mitigated by adopting\nthe aforementioned nod-based confirmation strategy for longer expressions.\nThe discussion on this issue has now been added to the Discussion section:\n“Finally, the current end-of-utterance detection strategy—five consecutive blank\ntokens—ensures zero-delay operation and aligns with the behavioral tendencies of\ndysarthric stroke patients, who often prefer short, low-effort expressions. For users\nwho wish to communicate longer or pause-rich utterances, the system can readily\nincorporate an explicit user-controlled cue (e.g., a single nod), which our gesture\ninterface already supports. This modification enables users to indicate expression\ncompletion directly, maintaining the system’s real-time responsiveness while\nimproving robustness to mid-utterance pauses. Moreover, as the decoding is\nperformed on a fixed sliding window (144 ms per token), the per-token latency and\ndecoding accuracy remain stable regardless of sentence length.”\nComment 6: The LLM agents (TSA and SEA) are responsible for critical error\ncorrection and sentence enrichment, significantly boosting user satisfaction. However,\nthe prompts and few-shot examples used are not provided in the manuscript. It is\nsuggested to include the exact prompt templates and examples as supplementary\ninformation. This is crucial for reproducibility and for understanding potential biases\nthe LLM might introduce into the synthesized speech.\nReply: We thank you for this valuable suggestion. We fully agree that reproducibility\nand transparency of the LLM agents are critical for understanding both performance\nand potential biases. In the revised manuscript, we have added a new Note S4 to\nprovide the complete prompt templates and few-shot examples used for both the\nToken Synthesis Agent (TSA) and the Sentence Expansion Agent (SEA).\nSpecifically, Note S4 now presents:\n1. The full prompt text for the TSA, including the system instruction, task\ndescription, and few-shot examples showing input token sequences and corresponding\nword outputs.\n2. The full prompt text for the SEA, detailing how emotion labels, time, and\nenvironmental context are integrated to produce enriched, contextually coherent\nsentences.\n3. Example pairs of model input and output for both agents to illustrate their\nbehavior and correction capability.\nThese additions ensure full reproducibility of the TSA and SEA configuration, while\nallowing readers to better understand how prompt design affects synthesis accuracy\nand bias control. Due to the length of Note S4, the full content is not duplicated here\nbut can be found in the Supplementary Information.\nReviewer #3\nComment 1: I recommend the work for publication and only note one thing that\nwasn't clear. How are pulse signals interpreted to infer emotions? A high pulse could\nindicate fear, excitement, or even physical exertion.\nReply: We thank you for this insightful question. The emotion inference in our\nsystem is not based on absolute pulse amplitude but on the temporal dynamics of the\ncarotid pulse waveform, particularly the R-R interval (RRI) sequence. Emotional\nchanges are known to modulate autonomic nervous activity, which in turn alters the\nsubtle temporal structure of the RRI [1, 2]. Our machine learning model is trained to\ncapture and map these RRI-based temporal representations to corresponding\nemotional states, thereby learning data-driven associations rather than relying on\nsimple heart rate metrics.\nWe also fully acknowledge your point regarding the possible confounding effect of\nphysical exertion. This factor was carefully considered in our experimental design.\nSince our target users are stroke patients with dysarthria, their limited mobility\nnaturally minimizes motion artefacts and exertion-induced variations in pulse signals.\nThis physiological context makes pulse-based emotion decoding feasible using a\nsingle sensing modality. To preserve system compactness and comfort, we therefore\nopted for a single-channel pulse input instead of multimodal fusion. Even with this\nstreamlined configuration, the classifier achieved an accuracy of 83.2%, validating\nthat RRI dynamics contain sufficient discriminative information for reliable emotion\ndecoding within our target population.\n[1] Kop, W. J. et al. Autonomic nervous system reactivity to positive and negative\nmood induction: The role of acute psychological responses and frontal electrocortical\nactivity. Biological Psychology 86, 230-238 (2011).\n[2] Jauniaux, J. et al. Emotion regulation of others’ positive and negative emotions is\nrelated to distinct patterns of heart rate variability and situational empathy. PloS One\n15, e0244427 (2020).\nTo make it clearer, we also added the following highlighted contents in the first\nparagraph of the section Decoding of emotional states:\n“To enrich sentence coherence by providing emotional context, we decode emotional\nstates from carotid pulse signals. Emotional changes modulate autonomic nervous\nactivity, which in turn alters the temporal structure of the R-R interval (RRI) within\npulse signals, forming measurable physiological representations of affective states\n[46]. Our machine learning model establishes a direct mapping between these\nRRI-based temporal representations and corresponding emotional categories.”\nComment 2: It would be great if the code was available (I may have missed it)\nReply: We thank you for this helpful comment. The code and core dataset supporting\nthis work are publicly available at our GitHub repository:\nhttps://github.com/tcy21414/Intelligent-Throat.\nWe have now made this information clearer in the revised manuscript, and it will\nappear in the Code availability and Data availability statements in the final published\nversion following Nature Communications data sharing standards."}, {"round": 2, "reviewer_comments": {"reviewer_1": "1. While I appreciate the added analysis on viseme-similar word pairs, the reported performance remains surprisingly high given that distinguishing such pairs is known to be a fundamentally difficult problem in this field. It is somewhat unclear how the system achieves this level of discriminability, especially in a dysarthric patient cohort, where articulatory variability is typically substantial. To better understand and validate this result, I would like to see the raw waveform patterns for these viseme-similar examples and relevance-weighted class activation maps (or an equivalent interpretability method) showing which portions of the raw signal the algorithm model relies on to differentiate these highly similar articulatory gestures.\n2. The explanation of how the 47 words were selected remains insufficient. The authors originally were asked to clarify the sampling protocol—including the use of a fixed random seed, a reproducible computational pick, and evidence supporting selection-bias mitigation—but the current response only gives a high-level statement that the 350 words were categorized and “randomly” sampled. It is still unclear how the categories were defined, how many words were drawn from each category, whether the selection was computational with a fixed seed or manually chosen after categorization, and how this process can be independently verified. A clear and reproducible description of the actual sampling pipeline is still needed.", "reviewer_2": "The authors have satisfactorily addressed all my comments. I am pleased to recommend acceptance of the manuscript.", "reviewer_3": "Thank you to the authors for addressing my prior concerns."}, "author_response": "NCOMMS-25-62725A R2 Revision Point-by-point Response\nWearable intelligent throat enables natural speech in stroke patients with dysarthria\nReviewer #1\nComment 1: While I appreciate the added analysis on viseme-similar word pairs, the\nreported performance remains surprisingly high given that distinguishing such pairs is\nknown to be a fundamentally difficult problem in this field. It is somewhat unclear\nhow the system achieves this level of discriminability, especially in a dysarthric\npatient cohort, where articulatory variability is typically substantial. To better\nunderstand and validate this result, I would like to see the raw waveform patterns for\nthese viseme-similar examples and relevance-weighted class activation maps (or an\nequivalent interpretability method) showing which portions of the raw signal the\nalgorithm model relies on to differentiate these highly similar articulatory gestures.\nReply: We sincerely thank you for this critical observation. We fully agree that\ndiscriminating viseme-similar word pairs—such as Book-Look or Metal-Medal—\nposes a fundamental challenge in speech recognition, particularly in dysarthric\npopulations where articulatory variability is substantial.\nTo address this concern, we wish to emphasize two key factors that contribute to the\nobserved discriminability in our system:\n(1) Building on prior evidence:\nOur previous-generation system based on a wearable choker [1] demonstrated that\nlaryngeal surface strain signals can capture sufficient articulatory dynamics to\nseparate viseme-confusable words, achieving ~93% classification accuracy in healthy\nusers. This suggested that even in the absence of vocal output, subtle articulatory\ndifferences leave distinguishable signatures at the neck surface.\n(2) Advancing to a two-stage decoding architecture:\nThe Intelligent Throat improves upon this by introducing a token-level decoding\nframework followed by a large language model (LLM) agent for sentence synthesis.\nThis two-stage process increases robustness for subtle phonemic contrasts by (i)\nreducing end-to-end dependency on single-word classification errors, and (ii)\nleveraging majority-voting prompting strategies across multiple token hypotheses to\nstabilize the decoding [2], which effectively amplifies stable articulatory cues while\nsuppressing noise-induced fluctuations, providing improved separability for\nphonemically similar tokens. This architecture is particularly advantageous for\ndysarthric users, as it accommodates intra-subject variability and mitigates the impact\nof isolated articulation anomalies.\nTo directly respond to your request for interpretability evidence, we have added\nSupplementary Fig. S17, which visualizes both the raw waveforms and the\ncorresponding 1D Grad-CAM relevance maps [3] for four viseme-similar word pairs\ntested in our dataset. These visualizations show that the model consistently attends to\nthe core articulatory segments (typically within the 0.8–1.8s interval) and allocates\nhigh relevance precisely in regions where the paired words diverge (e.g., the\nbeginning part of the dessert‑desert pair). The attention maps do not highlight noise\nartefacts or blank areas without articulation, reinforcing that the network’s decisions\nare grounded in physiologically meaningful deformation patterns.\n[1] Tang, C. et al. Ultrasensitive textile strain sensors redefine wearable silent speech\ninterfaces with high machine learning efficiency. npj Flexible Electronics 8, 27\n(2024).\n[2] Ahmed, T. and Devanbu, P. Better patching using llm prompting, via\nself-consistency. In 2023 38th IEEE/ACM International Conference on Automated\nSoftware Engineering (ASE), 1742-1746 (2023)\n[3] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via\ngradient-based localization. In Proceedings of the IEEE International Conference on\nComputer Vision, 618-626 (2017).\nFig. S17: Raw waveform patterns and relevance-weighted activation maps\n(Grad-CAM) for four viseme-similar word pairs.\nThe following contents have also been added in the Results Section:\n“To further evaluate the discriminability of the IT system on visually and\narticulatorily similar word pairs, we analyzed five viseme-similar pairs\n(increase/decrease, ship/sheep, book/look, metal/medal, and dessert/desert). The\nmodel achieved an average per-word accuracy of 96.3%, with pairwise confusion\nrates below 8%, indicating that the system can reliably distinguish between look-alike\nmouth shapes and subtle articulatory gestures. The detailed confusion matrix is shown\nin Fig. S16. To understand how the system achieves such discriminability, we\nvisualized the raw strain signals and Grad-CAM relevance maps for representative\nword pairs. As shown in Supplementary Fig. S17, the model consistently focuses on\nthe key articulatory segments where the target words diverge, such as the onset\nregions in the dessert/desert or book/look pairs. These attention maps confirm that the\npredictions are driven by meaningful physiological patterns rather than incidental\nnoise or silence segments.”\nComment 2: The explanation of how the 47 words were selected remains insufficient.\nThe authors originally were asked to clarify the sampling protocol—including the use\nof a fixed random seed, a reproducible computational pick, and evidence supporting\nselection-bias mitigation—but the current response only gives a high-level statement\nthat the 350 words were categorized and “randomly” sampled. It is still unclear how\nthe categories were defined, how many words were drawn from each category,\nwhether the selection was computational with a fixed seed or manually chosen after\ncategorization, and how this process can be independently verified. A clear and\nreproducible description of the actual sampling pipeline is still needed.\nReply: We thank you for this valuable comment and fully agree that a clearer and\nreproducible description of the corpus-construction procedure is necessary. In the\nrevised manuscript, we have substantially clarified the complete sampling pipeline to\nremove any ambiguity. Specifically, we now explain that the vocabulary was not\nselected by manually categorizing words, but instead derived through a fully\ncomputational, sentence-first procedure. From the therapist-curated sentence pool\nused in daily rehabilitation sessions, we randomly selected 20 sentences using a fixed\nrandom seed (seed = 42). The final 47 words were then defined as the complete set of\nunique lexical items that constitute these sampled sentences. This approach ensures\nunbiased selection, transparent criteria, and full reproducibility, since the corpus can\nbe independently regenerated by applying the same seed to the therapist-provided\nsentence pool. The Methods (Study Design section) has been updated accordingly\nwith the following additions:\n“A corpus was developed consisting of 47 Chinese words commonly used by stroke\npatients in daily communication and 20 sentences constructed from these words (see\nSupplementary Tables 2 and 3). The corpus originated from the set of sentences\nroutinely used by our collaborating speech-language therapists during daily\nrehabilitation sessions with dysarthric patients, representing several hundred\ntherapist-curated daily-communication sentences that collectively contain\napproximately 350 unique words. To ensure a transparent, unbiased, and reproducible\nsampling protocol, we performed a computational random selection of 20 sentences\nfrom this therapist-provided sentence pool using a fixed random seed (seed = 42). The\nfinal 47 words represent the complete set of unique vocabulary items that constitute\nthese 20 selected sentences, forming a minimal but comprehensive vocabulary that\ncaptures the core communicative intents encountered in real therapy scenarios. This\nprocess can be independently reproduced by applying the same seed to the\ntherapist-curated sentence pool and extracting the unique lexical items that compose\nthese sampled sentences.”\nReviewer #2\nComment: The authors have satisfactorily addressed all my comments. I am pleased\nto recommend acceptance of the manuscript.\nReply: We sincerely appreciate your positive assessment and your constructive\nfeedback throughout the review process, which has greatly strengthened the\nmanuscript.\nReviewer #3\nComment: Thank you to the authors for addressing my prior concerns.\nReply: Thank you for your thoughtful comments and for acknowledging our revisions;\nyour insights were valuable in improving the clarity and quality of the work."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed all of my previous concerns. I believe the manuscript is now suitable for publication in Nature Communications."}, "author_response": ""}]

Decision letter