GenAI4ELab/papercli-papers huggingface dataset:
Investigating the Impact of Word Informativeness on Speech Emotion Recognition
Authors: Sofoklis Kakouros
Venue: Interspeech 2025 (main)

Abstract: In emotion recognition from speech, a key challenge lies in identifying speech signal segments that carry the most relevant acoustic variations for discerning specific emotions. Traditional approaches compute functionals for features such as energy and F0 over entire sentences or longer speech portions, potentially missing essential fine-grained variation in the long-form statistics. This research investigates the use of word informativeness, derived from a pre-trained language model, to identify semantically important segments. Acoustic features are then computed exclusively for these identified segments, enhancing emotion recognition accuracy. The methodology utilizes standard acoustic prosodic features, their functionals, and self-supervised representations. Results indicate a notable improvement in recognition performance when features are computed on segments selected based on word informativeness, underscoring the effectiveness of this approach.
Lexical stress affects lenition: The case of Italian palato-alveolar affricates
Authors: Bowei Shao; Philipp Buech; Anne Hermes; Maria Giavazzi
Venue: Interspeech 2025 (main)

Abstract: The realization of palato-alveolar affricates in Italian varies regionally. While affricates have undergone deaffrication in Tuscan and southern varieties  (/ËtÍ¡ÊeË.na/ â [ËÊeË.na]), northern varieties are traditionally described as retaining the affricate. We provide acoustic and articulatory (EMA) evidence of an incipient lenition process in non-deaffricating varieties. This process is stress-conditioned as it is blocked in post-tonic affricates. Five of fifteen speakers deaffricated far-from-stress affricates in nonce words, as indicated by higher RMS amplitude and energy during the closure, while post-tonic affricates were preserved. Ten speakers did not exhibit stress-conditioned deaffrication. Both groups showed a longer acoustic closure duration, caused by delayed articulatory target achievement in post-tonic position. We discuss the phonological implications of these findings and their potential role in sound change.
Evaluation of a model for sound radiation from the vocal tract wall
Authors: Peter Birkholz; Tianyi Zhang
Venue: Interspeech 2025 (main)

Abstract: This study evaluated a simulation model for sound radiation from the vocal tract wall in the context of articulatory speech synthesis. In this model, the vocal tract is represented in terms of incremental contiguous tube sections, where the wall of each section reacts to the local sound pressure like a damped spring-mass systems. The surface-radiated sound from this model was compared with that of six real speakers by means of the voicebars of /b,d,g/ in different vowel contexts. Based on this, the wall parameters of the real speakers were estimated by fitting the simulated to the real voicebars. The parameter optimization allowed a close reproduction of the natural voicebar spectra with root-mean-square errors between 2.26 dB and 3.82 dB in the frequency range from 0-800 Hz. Hence, despite its simplicity, the modeling method is very well suited for articulatory speech synthesis.
FROST-EMA: Finnish and Russian Oral Speech Dataset of Electromagnetic Articulography Measurements with L1, L2 and Imitated L2 Accents
Authors: Satu Hopponen; Tomi Kinnunen; Alexandre Nikolaev; Rosa GonzÃ¡lez HautamÃ¤ki; Lauri Tavi; Einar Meister
Venue: Interspeech 2025 (main)

Abstract: We introduce a new FROST-EMA (Finnish and Russian Oral Speech Dataset of Electromagnetic Articulography) corpus. It consists of 18 bilingual speakers, who produced speech in their native language (L1), second language (L2), and imitated L2 (fake foreign accent). The new corpus enables research into language variability from phonetic and technological points of view. Accordingly, we include two preliminary case studies to demonstrate both perspectives. The first case study explores the impact of L2 and imitated L2 on the performance of an automatic speaker verification system, while the second illustrates the articulatory patterns of one speaker in L1, L2, and a fake accent.
Modeling Formant Dynamics in Mandarin /ai/: Effects of Speech Style and Speech Rate
Authors: Yunzhuo Xiang; Jingyi Sun
Venue: Interspeech 2025 (main)

Abstract: This study examines the relationship between speech clarity and speech rate by modeling F1/F2 variation in the Mandarin diphthong /ai/ across different durations and speech styles. The corpus includes 20 hours of conversational and 6 hours of read speech. Vowel durations were manually verified, and formant values were extracted using auto-correlation. Generalized additive mixed models (GAMMs) were used to examine the interaction between duration and formants. Results show that both read and slow speeches exhibit higher F1 onset and F2 offset for /ai/. However, read speech has larger formant frequency range, and even at the shortest (20 ms) or longest (200 ms) durations, diphthongs in read speech remained more clearly articulated than those in conversational speech of the same length. This suggests that speech clarity and speech rate may be distinct dimensions rather than interchangeable factors.
Representation of Perceived Prosodic Similarity of Conversational Feedback
Authors: Livia Qian; Carol Figueroa; Gabriel Skantze
Venue: Interspeech 2025 (main)

Abstract: Vocal feedback (e.g., 'mhm', 'yeah', 'okay') is an important component of spoken dialogue and is crucial to ensuring common ground in conversational systems. The exact meaning of such feedback is conveyed through both lexical and prosodic form. In this work, we investigate the perceived prosodic similarity of vocal feedback with the same lexical form, and to what extent existing speech representations reflect such similarities. A triadic comparison task with recruited participants is used to measure perceived similarity of feedback responses taken from two different datasets. We find that spectral and self-supervised speech representations encode prosody better than extracted pitch features, especially in the case of feedback from the same speaker. We also find that it is possible to further condense and align the representations to human perception through contrastive learning.
Prolongation in Romanian
Authors: Oana Niculescu; Monica Vasileanu
Venue: Interspeech 2025 (main)

Abstract: In this study we investigate segmental prolongation (PR) as a form of disfluent hesitation in a corpus of spontaneous Romanian monologues. A total of 3541 PRs were extracted from 216 minutes of speech pertaining to 4 native speakers (2 female, 2 male). In line with the methodology employed by previous corpus-studies on PR, our data reveal that prolonged segments have an average duration of 316ms (sd = 130), surfacing at a frequency of 11.3 per 100 words and following a 17â7â76% position distribution. In Romanian, all segments can undergo PR, with vowels being the preferred target (57.2%), followed by fricatives (12.8%), nasals (11.8%), plosives (10.3%), diphthongs (5.5%), affricates (1.6%) and laterals (0.8%). The vast majority of PRs surface in monosyllabic words (59%). Function words are prolonged in 57% of the cases. By including data from a lesser-studied European language, this paper broadens our understanding of the formal regularities of PR in a cross-linguistic setting.
Speech Reduction in French: The Relationship Between Vowel Space and Articulation Dynamics
Authors: KÃ¼bra Bodur; Corinne Fredouille; Christine Meunier
Venue: Interspeech 2025 (main)

Abstract: Reduction is an inherent characteristic of conversations, reflecting the dynamic adaptability of language. This study examined the link between vowel space and non-lexicalized reductions (NLR) in spontaneous French speech. The hypothesis posited that speakers with smaller vowel spaces - indicating centralized, less distinct vowels - would produce more NLR, defined as temporally compressed speech zones. Results showed that smaller vowel space (pVSA) predicted greater NLR only when articulation rate was considered, highlighting an interaction between spatial and temporal speech dynamics. Articulation rate was the strongest predictor, supporting theories that link faster speech to reduced articulatory precision. Vowel Distinctiveness Index (VDI) did not significantly predict NLR, suggesting it reflects broader systemic patterns rather than local reductions. These findings emphasized the interplay of temporal dynamics and individual articulation strategies in shaping reduction in speech.
Stress in Spoken and Whistled Greek
Authors: Andre Batchelder-Schwab; Vasileios Michos; Jonathan Barnes
Venue: Interspeech 2025 (main)

Abstract: This paper presents experimental results testing vowels of a register of whistled Greek called Sfyria. We used a frame sentence and minimal pairs. Each participant performed the experiment in whistled Greek, then again in spoken Greek. The results suggest that all five vowel qualities of Greek remain distinct in the whistled register (/i/ /e/ /o/ through F0 alone; /a/ /u/ through intensity). While acoustic differences were found between stressed and unstressed versions of all spoken vowel qualities (namely intensity), correlates of a stress distinction were not found for /i/. This might be a ceiling effect from general high intensity of front vowels in the whistled register, and we tentatively suggest that stressed /i/ in Sfyria is not produced differently from its unstressed counterpart.
Leveraging Text and Speech Processing for Suicide Risk Classification in Chinese Adolescents
Authors: Justyna Krzywdziak; BartÅomiej Eljasiak; Joanna StÄpieÅ; MichaÅ ÅwiÄtek; Agnieszka Pruszek
Venue: Interspeech 2025 (main)

Abstract: The increasing prevalence of depression among young people is a growing global concern. Early detection and intervention are crucial, making the development of effective diagnostic tools essential. This work explores the use of advanced text and speech processing techniques to classify suicide risk within the context of the SpeechWellness Challenge (SW1) and verifies if speech can be used as a non-invasive and readily available mental health indicator. The analysis incorporated both linguistic features and audio-based methods for spontaneous speech and passage reading. For text classification, Large Language Models like Qwen2.5 and BERT were evaluated. For audio-based prediction, state-of-the-art speech processing models, including Whisper, Wav2Vec2 and HuBERT were employed. Furthermore, a multimodal approach combining both vocal and textual features was investigated. The results obtained in this research ranked among the highest in the challenge.
The 1st SpeechWellness Challenge: Detecting Suicide Risk Among Adolescents
Authors: Wen Wu; Ziyun Cui; Chang Lei; Yinan Duan; Diyang Qu; Ji Wu; Bowen Zhou; Runsen Chen; Chao Zhang
Venue: Interspeech 2025 (main)

Abstract: The 1st SpeechWellness Challenge (SW1) aims to advance methods for detecting current suicide risk in adolescents using speech analysis techniques. Suicide among adolescents is a critical public health issue globally. Early detection of suicidal tendencies can lead to timely intervention and potentially save lives. Traditional methods of assessment often rely on self-reporting or clinical interviews, which may not always be accessible. The SW1 challenge addresses this gap by exploring speech as a non-invasive and readily available indicator of mental health. We release the SW1 dataset which contains speech recordings from 600 adolescents aged 10-18 years. By focusing on speech generated from natural tasks, the challenge seeks to uncover patterns and markers that correlate with current suicide risk.
Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
Authors: Yifan Gao; Jiao Fu; Long Guo; Hong Liu
Venue: Interspeech 2025 (main)

Abstract: Early identification of suicide risk is crucial for preventing suicidal behaviors. As a result, the identification and study of patterns and markers related to suicide risk have become a key focus of current research. In this paper, we present the results of our work in the 1st SpeechWellness Challenge (SW1), which aims to explore speech as a non-invasive and easily accessible mental health indicator for identifying adolescents at risk of suicide. Our approach leverages large language model (LLM) as the primary tool for feature extraction, alongside conventional acoustic and semantic features. The proposed method achieves an accuracy of 74% on the test set, ranking first in the SW1 challenge. These findings demonstrate the potential of LLM-based methods for analyzing speech in the context of suicide risk assessment.
Predicting Adolescent Suicidal Risk from Multi-task-based Speech: An Ensemble Learning Approach
Authors: Xi Chen; Renzhe Yu; Yanshen Tan; Yiyi Li; Quan Qian; Ying Lin
Venue: Interspeech 2025 (main)

Abstract: Adolescent suicide is a pressing global public health issue. Timely identification of suicide risk is crucial. Traditional methods of assessing suicide risk are often limited by their reliance on subjective input and resource requirements. This paper aims to address these limitations by detecting suicide risk from multi-task-based speech, utilizing a dataset of 600 Chinese adolescents (age: 10-18 yr) provided by the 1st SpeechWellness Challenge. Our approach involved both acoustic and semantic features extracted by OpenSmile, Emotion2Vec, and a fine-tuned BERT-Chinese model. The base models were trained using XGBoost and SVM, etc., with hyperparameters tuned by Bayesian optimization. Then we implemented a multi-model and multi-task nested voting ensemble framework to integrate the base models, achieving a final test set accuracy of 0.63 (recall=0.74, F1â0.67). This work highlights the potential of voice-based biomarkers in mental health assessment.
In-context learning capabilities of Large Language Models to detect suicide risk among adolescents from speech transcripts
Authors: Filomene Roquefort; Alexandre Ducorroy; Rachid Riad
Venue: Interspeech 2025 (main)

Abstract: Early suicide risk detection in adolescents is critical yet hindered by scalability challenges of current assessments. This paper presents our approach to the first SpeechWellness Challenge (SW1), which aims to assess suicide risk in Chinese adolescents through speech analysis. Due to speech anonymization constraints, we focused on linguistic features, leveraging Large Language Models (LLMs) for transcript-based classification. Using DSPy for systematic prompt engineering, we developed a robust in-context learning approach that outperformed traditional fine-tuning on both linguistic and acoustic markers. Our systems achieved third and fourth places among 180+ submissions, with 0.68 accuracy (F1=0.7) using only transcripts. Ablation analyses showed that increasing prompt example improved performance (p=0.003), with varying effects across model types and sizes. These findings advance automated suicide risk assessment and demonstrate LLMs' value in mental health applications.
Language-Agnostic Suicidal Risk Detection Using Large Language Models
Authors: June-Woo Kim; Wonkyo Oh; Haram Yoon; Sung-Hoon Yoon; Dae-Jin Kim; Dong-Ho Lee; Sang-Yeol Lee; Chan-Mo Yang
Venue: Interspeech 2025 (main)

Abstract: Suicidal risk detection in adolescents is a critical challenge, yet existing methods rely on language-specific models, limiting scalability and generalization. This study introduces a novel language-agnostic framework for suicidal risk assessment with large language models (LLMs). We generate Chinese transcripts from speech using an ASR model and then employ LLMs with prompt-based queries to extract suicidal risk-related features from these transcripts. The extracted features are retained in both Chinese and English to enable cross-linguistic analysis and then used to fine-tune corresponding pretrained language models independently. Experimental results show that our method achieves performance comparable to direct fine-tuning with ASR results or to models trained solely on Chinese suicidal risk-related features, demonstrating its potential to overcome language constraints and improve the robustness of suicidal risk assessment.
Network of acoustic characteristics for the automatic detection of suicide risk from speech. Contribution to the 2025 SpeechWellness challenge by the Semawave team
Authors: Vincent P. Martin; Charles Brazier; Maxime Amblard; Michel Musiol; Jean-Luc Rouas
Venue: Interspeech 2025 (main)

Abstract: Suicide is a leading cause of death among young individuals. Although early detection and intervention are vital for preventing suicide attempts, current suicide risk assessments rely heavily on clinical interviews and questionnaires, both of which are subject to patient biases. In contrast, speech analysis provides several objective advantages for estimating suicide risk. This is the focus of the 2025 SpeechWellness Challenge. This article presents a new paradigm for speech analysis based on network analyses of low-level descriptors. We evaluate the performance of this approach compared to the classical eGeMAPS+SVM model for suicide risk detection. Additionally, we assess the relevance of comparing networks derived from reading and spontaneous speech, and explore different methods for network construction, analyzing their respective performances.
ProMode: A Speech Prosody Model Conditioned on Acoustic and Textual Inputs
Authors: Eray Eren; Qingju Liu; Hyeongwoo Kim; Pablo Garrido; Abeer Alwan
Venue: Interspeech 2025 (main)

Abstract: Prosody conveys rich emotional and semantic information of the speech signal as well as individual idiosyncrasies. We propose a stand-alone model that maps text-to-prosodic features such as F0 and energy and can be used in downstream tasks such as TTS. The ProMode encoder takes as input acoustic features and time-aligned textual content, both are partially masked, and obtains a fixed-length latent prosodic embedding. The decoder predicts acoustics in the masked region using both the encoded prosody input and unmasked textual content. Trained on the GigaSpeech dataset, we compare our method with state-of-the-art style encoders. For F0 and energy predictions, we show consistent improvements for our model at different levels of granularity. We also integrate these predicted prosodic features into a TTS system and conduct perceptual tests, which show higher prosody preference compared to the baselines, demonstrating the modelâs potential in tasks where prosody modeling is important.
Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models
Authors: Kyowoon Lee; Artyom Stitsyuk; Gunu Jho; Inchul Hwang; Jaesik Choi
Venue: Interspeech 2025 (main)

Abstract: Recent advances in Text-to-Speech (TTS) have significantly improved speech naturalness, increasing the demand for precise prosody control and mispronunciation correction. Existing approaches for prosody manipulation often depend on specialized modules or additional training, limiting their capacity for post-hoc adjustments. Similarly, traditional mispronunciation correction relies on grapheme-to-phoneme dictionaries, making it less practical in low-resource settings. We introduce Counterfactual Activation Editing, a model-agnostic method that manipulates internal representations in a pre-trained TTS model to achieve post-hoc control of prosody and pronunciation. Experimental results show that our method effectively adjusts prosodic features and corrects mispronunciations while preserving synthesis quality. This opens the door to inference-time refinement of TTS outputs without retraining, bridging the gap between pre-trained TTS models and editable speech synthesis.
Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis
Authors: Paul Mayer; Florian Lux; Alejandro PÃ©rez-GonzÃ¡lez-de-Martos; Angelina Elizarova; Lindsey Vanderlyn; Dirk VÃ¤th; Ngoc Thang Vu
Venue: Interspeech 2025 (main)

Abstract: While generative methods have progressed rapidly in recent years, generating expressive prosody for an utterance remains a challenging task in text-to-speech synthesis. This is particularly true for systems that model prosody explicitly through parameters such as pitch, energy, and duration, which is commonly done for the sake of interpretability and controllability. In this work, we investigate the effectiveness of stochastic methods for this task, including Normalizing Flows, Conditional Flow Matching, and Rectified Flows. We compare these methods to a traditional deterministic baseline, as well as to real human realizations. Our extensive subjective and objective evaluations demonstrate that stochastic methods produce natural prosody on par with human speakers by capturing the variability inherent in human speech. Further, they open up additional controllability options by allowing the sampling temperature to be tuned.
GST-BERT-TTS: Prosody Prediction Without Accentual Labels For Multi-Speaker TTS Using BERT With Global Style Tokens
Authors: Tadashi Ogura; Takuma Okamoto; Yamato Ohtani; Erica Cooper; Tomoki Toda; Hisashi Kawai
Venue: Interspeech 2025 (main)

Abstract: Prosody prediction is crucial for pitch-accent languages like Japanese in text-to-speech (TTS) synthesis. Traditional methods rely on accent labels, which are often incomplete and do not generalize well. BERT-based models, such as fo-BERT, enable fundamental frequency prediction without accent labels but have been limited to single-speaker TTS. We propose GST-BERT-TTS, a novel method for multi-speaker TTS that integrates speaker-specific style embeddings from global style tokens (GST) into the token embeddings in BERT. The proposed method can realize speaker-aware fundamental frequency (fo) prediction in an accent label-free setting. Additionally, we extend fo-BERT to predict not only log fo but also energy and duration, improving speech expressiveness. Experiments using a Japanese multi-speaker TTS corpus demonstrate that GST-BERT-TTS improves the prosody prediction accuracy and synthesis quality compared with fo-BERT.
ExagTTS: An Approach Towards Controllable Word Stress Incorporated TTS for Exaggerated Synthesized Speech Aiding Second Language Learners
Authors: Anindita Mondal; Monica Surtani; Anil Kumar Vuppala; Parameswari Krishnamurthy; Chiranjeevi Yarra
Venue: Interspeech 2025 (main)

Abstract: Computer-Assisted Language Learning systems provide speech exaggerating at mispronounced word locations as a feedback to the L2 learners. Traditionally, expert speakers recordings (which limit the scalability) are considered for this task though there are advancements in text-to-speech (TTS) that can generate native like natural sounding speech. To address these, this work proposes two novel controllable strategies for scalable speech exaggeration. One strategy is direct speech exaggeration that incorporates the proposed label conditioned tokenization in GlowTTS. Another strategy is cascading the state-of-the-art TTS to a WORLD vocoder with proposed energy and duration modifications. A subset of Tatoeba corpus, that we annotated with prominent words, is used for experimentation. Automatic and manual assessment reveals that the exaggerated speech quality from both direct and cascaded strategy with duration modification is closer to the prominent words in the native speaker's speech.