GenAI4ELab/papercli-papers huggingface dataset:
Talker Normalization in Chinese Bilinguals: A Comparative Study
Authors: Mingxi LU; Ran Tao; Yujia Tian
Venue: Interspeech 2025 (main)

Abstract: Talker normalization enables listeners to adapt to speaker-specific acoustic variability, thereby facilitating speech perception across different talkers. While previous research suggests that talker normalization can occur in both Mandarin and Cantonese speech contexts, the extent to which it applies to non-linguistic contexts remains unclear. This study examined talker normalization in Mandarin-Cantonese bilinguals, comparing its effects in speech and nonspeech contexts. We recruited 36 bilingual participants to complete a forced-choice word identification task in one Mandarin paradigm and two Cantonese paradigms. The results revealed that under all three paradigms, tone normalization occurred robustly in the speech condition but was absent in the nonspeech condition. These findings provide novel evidence that talker normalization operates primarily within linguistic boundaries, supporting the view that talker normalization is language-specific rather than domain-general.
Coping with segmentalâprosodic incongruity in spoken word recognition in Japanese
Authors: Terumichi Ariga
Venue: Interspeech 2025 (main)

Abstract: A spoken word consists of two phonological cues: segments (phonemes) and prosody (e.g., lexical pitch accent). While both segments and prosody contribute to spoken word recognition, it is unknown how listeners interpret a spoken word when these cues contradict each other. For example, a word *tansu HLL is logically interpreted as both a segmental mispronunciation of dansu HLL "dance" and a prosodic mispronunciation of tansu LHH "wardrobe". The present study investigated how Japanese listeners interpret those segmentally and prosodically incongruent words in spoken word recognition using a cross-modal repetition priming paradigm. When ISI = 0 ms, listeners' lexical decision was delayed more for prosodic mispronunciation (prime: *tansu HLL; target: tansu LHH "wardrobe") than for segmental mispronunciation (prime: *dansu LHH; target: tansu LHH). In contrast, when ISI = 750 ms, listenersâ lexical decision was delayed more for segmental mispronunciation than for prosodic mispronunciation. These results demonstrate that lexical access to segmentally and prosodically incongruent words is a dynamic process, in which listeners first try a prosody-oriented interpretation and then shift to a segment-oriented interpretation.
What the Filler? Both ASR Systems and Humans Struggle More With Other Kinds of Disfluencies Than With Filler Particles
Authors: Saskia Wepner; Lucas Eckert; Gernot Kubin; Barbara Schuppler
Venue: Interspeech 2025 (main)

Abstract: Since disfluencies are frequent in conversational speech, they have received notable attention: from speech technologists to make automatic speech recognition (ASR) more robust and from speech scientists to learn more about human speech processing. For ASR, the most established quality measure is the word error rate (WER), while for human recognition, one of the measures is the recall of words or utterance-level semantics. We conduct a transcription experiment in which we present the same disfluent utterances to 54 participants and nine ASR systems. We analyse which factors affect transcription in the context of syntactic disfluencies and filler particles, including well-known factors such as pronunciation variation and articulation rate. We find that, surprisingly, both humans and ASR struggle with largely the same characteristics of conversational speech â despite their mean WERs differing by about 10% â and that the presence or absence of filler particles does not affect the WER.
Non-intrusive Speech Quality Assessment with Diffusion Models Trained on Clean Speech
Authors: Danilo de Oliveira; Julius Richter; Jean-Marie Lemercier; Simon Welker; Timo Gerkmann
Venue: Interspeech 2025 (main)

Abstract: Diffusion models have found great success in generating high quality, natural samples of speech, but their potential for density estimation for speech has so far remained largely unexplored. In this work, we leverage an unconditional diffusion model trained only on clean speech for the assessment of speech quality. We show that the quality of a speech utterance can be assessed by estimating the likelihood of a corresponding sample in the terminating Gaussian distribution, obtained via a deterministic noising process. The resulting method is purely unsupervised, trained only on clean speech, and therefore does not rely on annotations. Our diffusion-based approach leverages clean speech priors to assess quality based on how the input relates to the learned distribution of clean data. Our proposed log-likelihoods show promising results, correlating well with intrusive speech quality metrics and showing the best correlation with human scores in a listening experiment.
SQ-AST: A Transformer-Based Model for Speech Quality Prediction
Authors: Wafaa Wardah; Robert P. Spang; Vincent Barriac; Jan Reimes; Anna Llagostera; Jens Berger; Sebastian MÃ¶ller
Venue: Interspeech 2025 (main)

Abstract: We present SQ-AST, a transformer-based model for non-intrusive speech quality prediction. The model predicts overall speech quality and four perceptual dimensionsânoisiness, discontinuity, coloration, and loudnessâusing only the degraded signal. SQ-AST leverages Audio Spectrogram Transformers (AST), pretrained on large-scale audio datasets and fine-tuned on diverse speech quality corpora. It operates on short speech clips (4â12 seconds) without requiring a reference signal. Training was conducted on 106 databases comprising 165,791 samples. Independent evaluations confirm strong generalization to real-world conditions. The model is currently under consideration for ITU-T standardization, highlighting its potential for benchmarking, quality assessment, and industry adoption.
AttentiveMOS: A Lightweight Attention-Only Model forSpeech Quality Prediction
Authors: Imran E Kibria; Donald S. Williamson
Venue: Interspeech 2025 (main)

Abstract: Research in modeling subjective metrics for quality assessment has led to the development of no-reference speech models that directly operate on utterance waveforms to predict the Mean Opinion Score (MOS). These models often rely on convolutional layers for local feature extraction and embeddings from impractically large pretrained networks to enhance generalization. We propose an attention-only model based on Swin transformer and standard transformer layers to extract local context features and global utterance features, respectively. The self-attention operator excels at processing sequences, and our lightweight design enhances generalization on limited MOS datasets while improving real-world applicability. We train our network using a sequential self-teaching strategy to improve generalization on MOS labels affected by noise in listener ratings. Experiments on three datasets confirm the effectiveness of our design and demonstrate improvement over baseline models.
Universal Preference-Score-based Pairwise Speech Quality Assessment
Authors: Yu-Fei Shi; Yang Ai; Zhen-Hua Ling
Venue: Interspeech 2025 (main)

Abstract: To compare the performance of two speech generation systems, one of the most effective approaches is estimating the preference score between their generated speech. This paper proposes a novel universal preference-score-based pairwise speech quality assessment (UPPSQA) model, aimed at predicting the preference score between paired speech samples to determine which one has better quality. The model first predicts the absolute mean opinion score (MOS) for the two speech samples separately, and then aggregates them into a relative preference score using a preference function. To address the scarcity of preference data, we also construct a new pairwise speech dataset based on a MOS dataset for experiments. Experimental results confirm that, whether in training scenarios with different data types and label conditions, or in both in-domain and out-of-domain test scenarios, the prediction accuracy of UPPSQA outperforms that of the baseline models, demonstrating its universality.
FUSE-MOS: Fusion of Speech Embeddings for MOS Prediction with Uncertainty Quantification
Authors: Enjamamul Hoq; Nikhil Gupta; Danielle Omondi; Ifeoma Nwogu
Venue: Interspeech 2025 (main)

Abstract: The rapid advancements in text-to-speech (TTS) and voice conversion (VC) technologies necessitate evaluating the quality of synthesized speech. In this paper, we propose a novel network, FUSE-MOS, which combines the learned latent representations from raw audio waveforms and their corresponding Log-Mel spectrograms, to estimate the posterior distribution of Mean Opinion Score (MOS). Our method thus learns a broader and more nuanced representation of the speech signal. At inference, it predicts MOS value (point estimate) and also provides a measure of uncertainty of that prediction. By leveraging the combined latent representation, FUSE-MOS achieves significant improvements in performance metrics when compared to other existing approaches on benchmark datasets. We also explore an intelligent form of uncertainty filtering strategy to filter out low-confidence (high-uncertainty) samples. It shows FUSE-MOS's capability to maintain strong performance even with reduced data.
SHEET: A Multi-purpose Open-source Speech Human Evaluation Estimation Toolkit
Authors: Wen-Chin Huang; Erica Cooper; Tomoki Toda
Venue: Interspeech 2025 (main)

Abstract: We introduce SHEET, a multi-purpose open-source toolkit designed to accelerate subjective speech quality assessment (SSQA) research. SHEET stands for the Speech Human Evaluation Estimation Toolkit, which focuses on data-driven deep neural network-based models trained to predict human-labeled quality scores of speech samples. SHEET provides comprehensive training and evaluation scripts, multi-dataset and multi-model support, as well as pre-trained models accessible via Torch Hub and HuggingFace Spaces. To demonstrate its capabilities, we re-evaluated SSL-MOS, a speech self-supervised learning (SSL)-based SSQA model widely used in recent scientific papers, on an extensive list of speech SSL models. Experiments were conducted on two representative SSQA datasets named BVCC and NISQA, and we identified the optimal speech SSL model, whose performance surpassed the original SSL-MOS implementation and was comparable to state-of-the-art methods.
Investigating continuous autoregressive generative speech enhancement
Authors: Haici Yang; Gordon Wichern; Ryo Aihara; Yoshiki Masuyama; Sameer Khurana; FranÃ§ois G. Germain; Jonathan Le Roux
Venue: Interspeech 2025 (main)

Abstract: Following the success of autoregressive (AR) language models in predicting discrete tokens, it has become common practice for autoregressive audio and speech models to use discrete tokens generated by a neural audio codec. However, recent work has demonstrated that replacing discrete token probability modeling in an AR model with a continuous diffusion procedure can improve both model performance and efficiency for image generation. In this paper, we explore applying such a diffusion loss to replace discrete token modeling in an AR generative speech enhancement model. We explore several important design choices, including comparing standard AR models with masked AR models, and mel spectrograms with learned latents as the continuous feature representation. Our results demonstrate the potential of continuous AR speech enhancement, particularly in cases of severe noise.
Dynamic Layer Gating for Speech Enhancement
Authors: Venkatesh Parvathala; K. Sri Rama Murty
Venue: Interspeech 2025 (main)

Abstract: Conventional deep learning-based speech enhancement models perform inference with fixed network structure. However, due to the time-varying spectral characteristics of speech and noise, the noisy signal exhibits varying subband and segmental signal-to-noise ratios. Adapting the network structure to these variations may offer computational efficiency and interpretability. This work introduces two gating mechanisms, sample-level gating (SG) and time-frequency level gating (TFG), to dynamically skip layers within the two-stage conformer blocks of the conformer-based metric GAN network. Our experiments demonstrate competitive performance while reducing multiply-accumulate operations by 36% with SG and 48% with TFG. Furthermore, we provide insights into the model behavior by analyzing the gate activations and intermediate layer outputs. Our analysis shows that initial layers prioritize low-frequency speech regions, while later layers focus on nonspeech regions and higher frequencies.
Model as Loss: A Self-Consistent Training Paradigm
Authors: Saisamarth Rajesh Phaye; Milos Cernak; Andrew Harper
Venue: Interspeech 2025 (main)

Abstract: Conventional methods for speech enhancement rely on handcrafted loss functions (e.g., time or frequency domain losses) or deep feature losses (e.g., using WavLM or wav2vec), which often fail to capture subtle signal properties essential for optimal performance. To address this, we propose Model as Loss, a novel training paradigm that utilizes the encoder from the same model as a loss function to guide the training.  The Model as Loss paradigm leverages the encoder's task-specific feature space, optimizing the decoder to produce output consistent with perceptual and task-relevant characteristics of the clean signal. By using the encoder's learned features as a loss function, this framework enforces self-consistency between the clean reference speech and the enhanced model output. Our approach outperforms pre-trained deep feature losses on standard speech enhancement benchmarks, offering better perceptual quality and robust generalization to both in-domain and out-of-domain datasets.
Test-Time Training for Speech Enhancement
Authors: Avishkar Behera; Riya Ann Easow; Venkatesh Parvathala; K. Sri Rama Murty
Venue: Interspeech 2025 (main)

Abstract: This paper introduces a novel application of Test-Time Training (TTT) for Speech Enhancement, addressing the challenges posed by unpredictable noise conditions and domain shifts. This method combines a main speech enhancement task with a self-supervised auxiliary task in a Y-shaped architecture. The model dynamically adapts to new domains during inference time by optimizing the proposed self-supervised tasks like noise-augmented signal reconstruction or masked spectrogram prediction, bypassing the need for labeled data. We further introduce various TTT strategies offering a trade-off between adaptation and efficiency. Evaluations across synthetic and real-world datasets show consistent improvements across speech quality metrics, outperforming the baseline model. This work highlights the effectiveness of TTT in speech enhancement, providing insights for future research in adaptive and robust speech processing.
Few-step Adversarial SchrÃ¶dinger Bridge for Generative Speech Enhancement
Authors: Seungu Han; Sungho Lee; Juheon Lee; Kyogu Lee
Venue: Interspeech 2025 (main)

Abstract: Deep generative models have recently been employed for speech enhancement to generate perceptually valid clean speech on large-scale datasets. Several diffusion models have been proposed, and more recently, a tractable SchrÃ¶dinger Bridge has been introduced to transport between the clean and noisy speech distributions. However, these models often suffer from an iterative reverse process and require a large number of sampling stepsâmore than 50. Our investigation reveals that the performance of baseline models significantly degrades when the number of sampling steps is reduced, particularly under low-SNR conditions. We propose integrating SchrÃ¶dinger Bridge with GANs to effectively mitigate this issue, achieving high-quality outputs on full-band datasets while substantially reducing the required sampling steps. Experimental results demonstrate that our proposed model outperforms existing baselines, even with a single inference step, in both denoising and dereverberation tasks.
Exploiting Bispectral Features for Single-Channel Speech Enhancement
Authors: Venkatesh Parvathala; Ramesh Gundluru; Sreekanth Sankala; K. Sri Rama Murty
Venue: Interspeech 2025 (main)

Abstract: Deep neural network-based speech enhancement (SE) algorithms often utilize an encoder - dual-path processing module - decoder architecture. The encoder, consisting of pointwise convolutions and a dilated densenet (DDN), maps time-frequency bins to a higher-dimensional space to better capture signal and noise characteristics but significantly adds to the computational complexity. In this work, we propose a computationally efficient modification by replacing the DDN block within the encoder with bispectral features, which effectively capture frequency correlations and phase information. We demonstrate the effectiveness of the method by incorporating it in two SE models, CMGAN and MP-SENet, and evaluating on two datasets, DNS-2020 and VoiceBank+DEMAND. Our experiments show that the proposed modification achieves similar or better performance while reducing the multiply-accumulate operations by 23.87% in CMGAN and 17.45% in MP-SENet, demonstrating its computational efficiency.
Automatic Dialectal Transcription: An Evaluation on Finnish and Norwegian
Authors: Olli Kuparinen
Venue: Interspeech 2025 (main)

Abstract: The fields of dialectology and sociolinguistics are highly reliant on phonetically transcribed spoken language data, which are often written in language-specific styles and alphabets. Transcribing dialectal speech phonetically is time-consuming and thus a major bottleneck for data collection in variational linguistics. Meanwhile, the field of automatic speech recognition (ASR) has taken leaps forward in recent years. In this work, we introduce automatic dialectal transcription as a distinct ASR task and investigate solutions using data from two unrelated languages (Finnish and Norwegian) with two levels of transcription precision. We find a large performance gap between the languages, as Finnish models are much more efficient than those for Norwegian. We further evaluate the character-level errors of both languages and dialectal difficulties of the best Finnish model. We find that the most geographically central dialects tend to be easier to transcribe than the more distant ones.
Can ASR generate valid measures of child reading fluency?
Authors: Wieke Harmsen; Roeland van Hout; Catia Cucchiarini; Helmer Strik
Venue: Interspeech 2025 (main)

Abstract: Early diagnosis of reading difficulties requires assessment of childrenâs oral reading fluency. Since obtaining reliable assessments through subjective rating rubrics has proven to be difficult and time consuming, the current study presents a novel procedure incorporating Automatic Speech Recognition (ASR) to compute 15 measures related to the aspects phrasing, smoothness and pacing of oral reading fluency. We investigate the validity of these measures by comparing them to the same measures computed on human transcripts. This comparison was performed on a dataset of 244 recordings of texts read by 131 Dutch primary school children (aged 6-13 years). We found strong correlations for 12 out of 15 measures, which emphasizes the great potential of these measures for more reliable, less time-consuming and sustainable reading assessment.
SGED-Probe: Probing E2E ASR decoder and aligner for spoken grammar error detection under three speaking practice conditions
Authors: Chowdam Venkata Thirumala Kumar; Chiranjeevi Yarra
Venue: Interspeech 2025 (main)

Abstract: Grammar error detection in one's speech, referred to as spoken grammar error detection (SGED), is a critical component for building computer-assisted language learning systems. Traditionally, ASR followed by text-based GED has been used, but ASR errors can limit SGED performance. In this work, we analyse SGED under three speaking conditions: spontaneous, semispontaneous, and memorized, using five state-of-the-art ASRs. We examine the decoded text and ASR cues such as confidence scores and aligner probabilities, out of which, for the latter one, the ground-truth grammatically correct (GGC) text is used. Experiments revealed that autoregressive ASRs show bias toward GGC text, leading to suboptimal performance. It is observed that there is an increase in performance in response to the decreased freedom of speech, with semi-spontaneous speech outperforming memorized speech. Aligner probabilities outperform confidence scores despite the alignment and overconfidence issues.
Evaluating Logit-Based GOP Scores for Mispronunciation Detection
Authors: Aditya Kamlesh Parikh; Cristian Tejedor-Garcia; Catia Cucchiarini; Helmer Strik
Venue: Interspeech 2025 (main)

Abstract: Pronunciation assessment relies on goodness of pronunciation (GOP) scores, traditionally derived from softmax-based posterior probabilities. However, posterior probabilities may suffer from overconfidence and poor phoneme separation, limiting their effectiveness. This study compares logit-based GOP scores with probability-based GOP scores for mispronunciation detection. We conducted our experiment on two L2 English speech datasets spoken by Dutch and Mandarin speakers, assessing classification performance and correlation with human ratings. Logit-based methods outperform probability-based GOP in classification, but their effectiveness depends on dataset characteristics. The maximum logit GOP shows the strongest alignment with human perception, while a combination of different GOP scores balances probability and logit features. The findings suggest that hybrid GOP methods incorporating uncertainty modeling and phoneme-specific weighting improve pronunciation assessment.
Towards a Unified Benchmark for Arabic Pronunciation Assessment: Qurâanic Recitation as Case Study
Authors: Yassine El Kheir; Omnia Ibrahim; Amit Meghanani; Nada Almarwani; Hawau Toyin; Sadeen Alharbi; Modar Alfadly; Lamya Alkanhal; Ibrahim Selim; Shehab Elbatal; Salima Mdhaffar; Thomas Hain; Yasser Hifny; Mostafa Shahin; Ahmed Ali
Venue: Interspeech 2025 (main)

Abstract: We present a unified benchmark for mispronunciation detection in Modern Standard Arabic (MSA) using Qur'anic recitation as a case study. Our approach lays the groundwork for advancing Arabic pronunciation assessment by providing a comprehensive pipeline that spans data processing, the development of a specialized phoneme set tailored to the nuances of MSA pronunciation, and the creation of the first publicly available test set for this task, which we term as the Qur'anic Mispronunciation Benchmark (QuranMB.v1). Furthermore, we evaluate several baseline models to provide initial performance insights, thereby highlighting both the promise and the challenges inherent in assessing MSA pronunciation. By establishing this standardized framework, we aim to foster further research and development in pronunciation assessment in Arabic language technology and related applications.
OMPAL: Bridging Speech and Learning with an Open-Source Mandarin Pronunciation Assessment Corpus for Global Learners
Authors: Wen-Wei Hsieh; Hao-Wei Chi; Kuan-Chen Wang; Ping-Cheng Yeh; Te-hsin Liu; Chen-Yu Chiang
Venue: Interspeech 2025 (main)

Abstract: This paper introduces OMPAL, a new open-source Mandarin corpus specifically designed for non-native pronunciation assessment. This corpus comprises 1,768 Mandarin utterances from French L1 speakers learning Mandarin, each meticulously annotated by four experts with professional Mandarin teaching experience at both the word and sentence levels. We also provide a manual scoring system to assist researchers in constructing related corpora. Furthermore, a baseline model for pronunciation assessment, which is publicly accessible, is provided alongside our corpus. The OMPAL corpus, available for commercial and non-commercial use, is designed to support and enhance speech research across various applications. We believe that OMPAL will be a valuable resource for the speech research community.