GenAI4ELab/papercli-papers huggingface dataset:
Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition
Authors: Chin Yuen Kwok; Jia Qi Yip
Venue: Interspeech 2025 (main)

Abstract: Contextual biasing improves rare word recognition of ASR models by prioritizing the output of rare words during decoding. A common approach is Trie-based biasing, which gives ``bonus scores" to partial hypothesis (e.g. ``Bon") that may lead to the generation of the rare word (e.g. ``Bonham"). If the full word (``Bonham") isnât ultimately recognized, the system revokes those earlier bonuses. This revocation is limited to beam search and is computationally expensive, particularly for models with large decoders. To overcome these limitations, we propose adapting ASR models to look ahead and predict multiple steps at once. This avoids the revocation step entirely by better estimating whether a partial hypothesis will lead to the generation of the full rare word. By fine-tuning Whisper with only 10 hours of synthetic data, our method reduces the word error rate on the NSC Part 2 test set from 30.86% to 12.19%.
Web-Based Application for Real-Time Biofeedback of Vocal Resonance in Gender-Affirming Voice Training: Design and Usability Evaluation
Authors: Tara McAllister; Collin Eagen; Yi Shan; Peter Traver; Daphna Harel; Tae Hong Park; Vesna Novak
Venue: Interspeech 2025 (main)

Abstract: Gender-affirming voice training (GAVT) can reduce voice-gender incongruence for transgender and gender-diverse individuals, but access is often limited by high costs and a lack of qualified providers. Interactive software could expand access, but existing GAVT apps are typically limited in functionality. This paper describes the development and testing of software to address one of the most challenging aspects of GAVT: modifying vocal tract resonance. We introduce a biofeedback system that uses a real-time linear predictive coding (LPC) spectrum to visualize changes in resonance as learners adjust their vocal tract shape. Visual targets for brighter (feminine) and darker (masculine) resonances help guide users toward their desired voice characteristics. In-lab user testing with 10 trans women yielded an average System Usability Scale (SUS) score of 75.25, supporting the acceptability of the tool as an adjunct resonance training in the GAVT context.
On the Production and Perception of a Single Speaker's Gender
Authors: Robin Netzorg; Naomi Carvalho; Andrea Guzman; Lydia Wang; Juliana Francis; Klo Vivienne Garoute; Keith Johnson; Gopala Anumanchipalli
Venue: Interspeech 2025 (main)

Abstract: A voice's gender is considered to be dictated by one's biology and cultural situation. Without modification, this determinism results in colinearity between acoustic metrics, making disentangling a metric's contribution to gender perception difficult. To study disentanglement on natural speech, we collaborate with a gender-affirming voice teacher to collect the Disentangled Source-Filter Dataset (DSFD): 45-minutes of audio along 25 Pitch, Resonance, and Weight voice configurations, coupled with Electroglottograph (EGG) measurements. Our analysis demonstrates certain acoustic and physical metrics, namely avg. $F_0$, $\Delta F$, Contact Quotient (CQ), and Loudness correlate with Pitch, Resonance, and Weight. Going on to perform perceptual studies of gender, naturalness, and realness, we see that $\Delta F$ is the strongest predictor of perceived gender. Perceived naturalness and realness of a voice, however, prove to be unpredictable by these acoustic metrics.
Conveying Gender Through Speech: Insights from Trans Men
Authors: Alice Ross; Cliodhna Hughes; Eddie L. Ungless; Catherine Lai
Venue: Interspeech 2025 (main)

Abstract: Trans people tend to be well aware of the ways in which (perceived) gender is indexically linked to the voice. Focusing on the understudied population of trans men, we present one of the first studies on style shift in trans speakers, considering the phonetic features of trans men's speech in different contexts, and their own beliefs about vocal cues to gender perception. Our participants (n = 7) provided read speech samples with contrasting imagined listeners, and discussed their voices and experiences in semi-structured interviews. Acoustic and qualitative analyses show that our participants adopted lower, narrower pitch ranges when they imagined speaking to a stranger, though individuals differed in the ways they altered their vowel formants and /s/ production. The interview data provide further insights into trans men's speech styles, self-monitoring, and pursuit of authenticity, highlighting shared concerns about voice training apps, safety, and representation for trans people.
Queer Waves: A German Speech Dataset Capturing Gender and Sexual Diversity from Podcasts and YouTube
Authors: Ingo Siegert; Jan Marquenie; Sven Grawunder
Venue: Interspeech 2025 (main)

Abstract: Developing equitable and inclusive speech technologies requires datasets that represent the full spectrum of human voices, including those of LGBTQIA+ speakers. However, capturing spontaneous, high-quality audio from marginalized gender and sexual identities presents significant ethical, logistical, and representational challenges. This paper introduces Queer Waves, a German speech corpus compiled from podcast and YouTube content featuring self-identified LGBTQIA+ speakers, with a particular focus on diverse gender identities and sexual orientations. We further address the legal and ethical considerations inherent in collecting sensitive personal data. The Queer Waves corpus comprises approximately 335 hours of speech from over 400 self-identified LGBTQIA+ speakers, spanning ages from 18 to 86 years. By expanding representation across a wide range of gender identities and orientations, Queer Waves aims to advance the development of fairer and more accurate speech technologies.
Reddit FlairShare: A Human-Annotated Dataset of Gender-Progressive Online Discourse
Authors: Carlos Hartmann
Venue: Interspeech 2025 (main)

Abstract: This paper presents a large-scale dataset capturing Reddit comments with pronoun declarations in the respective user flairs, offering a new resource for studying linguistic identity, gender expression, and digital discourse. Totaling 72 million tokens, it contains all comments by pronoun-declaring users to present a broader view of their language use than previous corpora that selected isolated utterances. The dataset enables research across multiple domains, including (online) sociolinguistics, natural language processing (NLP), and other social sciences. It facilitates the study of pronoun-sharing behavior, the distribution and adoption of non-binary pronouns, and the use of mixed pronouns in online discourse. Future work can expand the dataset to capture more rare pronoun declarations; nevertheless, it provides a highly curated, valuable foundation for the study of online gender expression and discourse, innovative language, and identity performance in digital spaces.
Voices of `cyborg awesomeness': Posthuman embodiment of nonbinary gender expression in AI speech technologies
Authors: Maxwell Hope; Ãva SzÃ©kely
Venue: Interspeech 2025 (main)

Abstract: Speech-generating devices (SGDs) provide users with text-to-speech (TTS) voices that shape identity and self-expression. Current TTS voices enable self-expression but often lack customizable features for authentic voice embodiment, particularly for nonbinary SGD users seeking gender affirmation as existing TTS voices largely reproduce binary, cisgender speech patterns. This study examines how nonbinary SGD users embody, or disembody, synthetic voices and the factors influencing voice affirmation. Through a survey, we analyze experiences of nonbinary SGD users and their impressions of generated speech samples, investigating the role of technological possibilities in gender affirmation and voice embodiment. Findings inform the creation of more user-centered TTS technologies, and challenge dominant paradigms in speech technology, gesturing toward a posthumanist rethinking of voice as co-constructed between human and machine.
Neutral Tone Variation in Beijing Mandarin: Is Neutral Tone Toneless?
Authors: Xiao Dong; Fengming Liu; Chien-Jer Lin; Monica Nesbitt; Shuju Shi
Venue: Interspeech 2025 (main)

Abstract: Neutral tone (NT) is a distinctive feature of Beijing Mandarin, traditionally described as toneless and entirely dependent on the preceding tone. Recent studies suggest that NT may retain specific phonetic targets and exists on a continuum of reduction, challenging the strict neutral versus full-tone dichotomy. This study examines the phonetic realization of NT as influenced by three factors - preceding tone, underlying tone, and NT type - using word-list reading data from 36 Beijing Mandarin speakers. Our findings confirm a robust effect of the preceding tone. In the meantime, we identify a significant impact of the underlying tone, indicating that NT is not entirely toneless but retains some degree of phonological specification. Moreover, the differences observed between optional and forbidden NT words suggest that NT should be conceptualized as part of a gradient system influenced by contextual factors, rather than as a simple neutral versus full-tone contrast.
The Role of Syntactic Structures in Shaping Directionality in Trisyllabic Tone Sandhi: Evidence from Tianjin Mandarin
Authors: Siqi Lu; Hui Feng; Ziyu Xiong
Venue: Interspeech 2025 (main)

Abstract: Prior studies reported inconsistent syntactic influence on trisyllabic tone sandhi directionality in Tianjin Mandarin. This study investigates the role of six syntactic structures and generational differences in the application of sandhi rules, analyzing seven trisyllabic tone patterns across two structural configurations ("2+1"/"1+2"), with data from 26 native speakers. Key findings are: 1) T4-T4-T1 pattern triggers right-edge sandhi, producing a novel T4-T2-T1 pattern. 2) T3-T1-T1 pattern exhibits one-round sandhi in VP+N, A+NP, N+NP, Q+NP, and V+NP structures, but two-round application in NP+N. 3) Diachronic shifts: the absence of T4-T4 sandhi and the replacement of T1-T1âT3-T1 with T1-T1âT2-T1 apply universally across generations, restructuring trisyllabic outcomes without altering directionality. These findings clarify syntax-sandhi interplay and generational variation in Tianjin Mandarin, revealing systematic directional patterns tied to syntactic structures.
Acoustic Representation and Realization of Weak Elements Subcategories: In the Case of Tianjin Mandarin
Authors: Zhijie Li; Hui Feng
Venue: Interspeech 2025 (main)

Abstract: Weak elements in Tianjin Mandarin are often assumed to have distinct origins and functions across subcategories, yet acoustic differences among them remain underexplored. This study analyzes the first two formants (F1, F2) and fundamental frequency (F0) of weak elements produced by six Tianjin Mandarin speakers. Results reveal: (1) Vowel quality shows minimal variation across subcategories, with F1 reduction and F2 centralization in monophthongs, and F1 centralization and compression in diphthongs. (2) Tone realization varies: affixes, locative prepositions, directional verbs, reduplicated words and optional weak elements exhibit a fixed mid-low pitch target, achieved through target-setting. In contrast, structural particles, aspectual particles, habitual weak elements, and functional weak elements rely on contextual tone spreading. These findings highlight two coexisting patterns of tone realization in Tianjin Mandarin weak elements.
Lexical competition in the process of Cantonese tone merging: Diverse Impact Mechanisms Across Different Individuals and Tone Pairs
Authors: Lishan Li; Yaolin Zhou; Xiaoying Xu
Venue: Interspeech 2025 (main)

Abstract: Lexical competition has been shown to exert an inhibitory influence on phonemic mergers in phonetic evolution. This study investigates the merging process of Cantonese tone pairs, examining the lexical competition effect from an individual perspective. Results reveal that lexical competition influences tone production even in speakers without clear mergers, helping maintain tonal contrasts while subtly altering pitch distributions, which may signal future change. For participants merging only one tone pair, the effect of lexical competition varies: it consistently inhibits merging for the T4 (21)-T6 (22) pair, but for the T3 (33)-T6 (22) pair, its influence manifests in three distinct patterns-no effect, inhibition, or promotion of merging. Conversely, in participants merging all three tones, lexical competition shows minimal impact. This research elucidates the diverse mechanisms through which lexical competition shapes tone-merging processes.
Tonal Perception in Changde Mandarin
Authors: Zhenrui Zhang; Fang Hu
Venue: Interspeech 2025 (main)

Abstract: This paper explores perceptual cues of the four tones in Changde Mandarin. Results show that T1 is perceived as a high-level tone, T2 is a low-rising tone in both production and perception, T3 is not phonologically level but falling, and the perception of T4 is influenced by various factors. And results also show that tonal perception might not be categorical, since neither the T2-T3 continuum nor the T2-T4 continuum fulfills the standards for typical categorical perception.
Tonal Contrasts in the Malipo Variety of the Mienic Language
Authors: Changhong Du; Fang Hu
Venue: Interspeech 2025 (main)

Abstract: This paper describes tonal contrasts in the Malipo variety of the Mienic language and explores how they are manifested by acoustic cues. Results show that Linear Mixed-Effects Models based on Growth Curve Analysis successfully characterizes the 5 level tones, 3 falling tones, 3 rising or concave tones, and 2 convex tones in the Malipo variety. In addition to F0, duration also plays a role in a complex tonal system.
Building an Accurate Open-Source Hebrew ASR System through Crowdsourcing
Authors: Yanir Marmor; Yair Lifshitz; Yoad Snapir; Kinneret Misgav
Venue: Interspeech 2025 (main)

Abstract: Automatic Speech Recognition (ASR) for Hebrew faces significant challenges due to limited resources and rich morphology. While recent advances have improved high-resource languages ASR, Hebrew still lacks robust open-source solutions. Through crowdsourcing efforts, we created a dataset of 314 hours of transcribed speech, which we used to train a new Hebrew ASR model based on the Whisper architecture. Our model demonstrates up to 29% reduction in error rates compared to existing Whisper solutions, particularly excelling in producing verbatim transcriptions. Additionally, we introduce a new evaluation dataset designed specifically for Hebrew ASR assessment. By making both the model and methodology freely available, we provide a framework that can be adapted for developing ASR systems in other under-resourced languages. This work represents a step toward making speech technology more accessible in different languages.
A Practitionerâs Guide to Building ASR Models for Low-Resource Languages: A Case Study on Scottish Gaelic
Authors: OndÅej Klejch; William Lamb; Peter Bell
Venue: Interspeech 2025 (main)

Abstract: An effective approach to the development of ASR systems for low-resource languages is to fine-tune an existing multilingual end-to-end model. When the original model has been trained on large quantities of data from many languages, fine-tuning can be effective with limited training data, even when the language in question was not present in the original training data. The fine-tuning approach has been encouraged by the availability of public-domain E2E models and is widely believed to lead to state-of-the-art results. This paper, however, challenges that belief. We show that an approach combining hybrid HMMs with self-supervised models can yield substantially better performance with limited training data. This combination allows better utilisation of all available speech and text data through continued self-supervised pre-training and semi-supervised training. We benchmark our approach on Scottish Gaelic, achieving WER reductions of 32% relative over our best fine-tuned Whisper model.
Automatic Speech Recognition for Low-Resourced Middle Eastern Languages
Authors: Razhan Hameed; Sina Ahmadi; Hanah Hadi; Rico Sennrich
Venue: Interspeech 2025 (main)

Abstract: Despite significant advancements in language and speech technologies, many languages in the Middle East remain under-served, leading to a technological disparity that negatively impacts these languages. This paper presents a pioneering effort to address this issue by focusing on speech technologies for low-resourced languages in the Middle East. We introduce a community-driven volunteer-based initiative to collect audio recordings for six languages spoken by an estimated population of 30 million speakers. Through this initiative, we collect over 40 hours of speech data, with 75% of utterances based on multilingual parallel corpora. In our experiments, we demonstrate the impact of data collection and fine-tuning models on the performance of speech technologies for these languages. This research serves as a crucial step towards preserving and promoting linguistic diversity in the Middle East while ensuring equal access to speech technologies for all language communities.
In-context Language Learning for Endangered Languages in Speech Recognition
Authors: Zhaolin Li; Jan Niehues
Venue: Interspeech 2025 (main)

Abstract: With approximately 7,000 languages spoken worldwide, current large language models (LLMs) support only a small subset. Prior research indicates LLMs can learn new languages for certain tasks without supervised data. We extend this investigation to speech recognition, investigating whether LLMs can learn unseen, low-resource languages through in-context learning (ICL). With experiments on four diverse endangered languages that LLMs have not been trained on, we find that providing more relevant text samples enhances performance in both language modelling and Automatic Speech Recognition (ASR) tasks. Furthermore, we show that the probability-based approach outperforms the traditional instruction-based approach in language learning. Lastly, we show ICL enables LLMs to achieve ASR performance that is comparable to or even surpasses dedicated language models trained specifically for these languages, while preserving the original capabilities of the LLMs.
CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset
Authors: Brian Yan; Injy Hamed; Shuichiro Shimizu; Vasista Sai Lodagala; William Chen; Olga Iakovenko; Bashar Talafha; Amir Hussein; Alexander Polok; Kalvin Chang; Dominik Klement; Sara Althubaiti; Puyuan Peng; Matthew Wiesner; Thamar Solorio; Ahmed Ali; Sanjeev Khudanpur; Shinji Watanabe
Venue: Interspeech 2025 (main)

Abstract: We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4 test sets which cover in total 113 unique code-switched language pairs across 52 languages: 1) a 14 X-English language pair set with real voices reading synthetically generated code-switched sentences, 2) a 16 X-English language pair set with generative text-to-speech 3) a 60 {Arabic, Mandarin, Hindi, Spanish}-X language pair set with the generative text-to-speech, and 4) a 45 X-English lower-resourced language pair test set with concatenative text-to-speech. Besides the three test sets, CS-FLEURS also provides a training set with 128 hours of generative text-to-speech data across 16 X-English language pairs. Our hope is that CS-FLEURS helps to broaden the scope of future code-switched speech research.
Weakly Supervised Data Refinement and Flexible Sequence Compression for Efficient Thai LLM-based ASR
Authors: Mingchen Shao; Xinfa Zhu; Chengyou Wang; Bingshen Mu; Hai Li; Ying Yan; Junhui Liu; Danming Xie; Lei Xie
Venue: Interspeech 2025 (main)

Abstract: Despite remarkable achievements, automatic speech recognition (ASR) in low-resource scenarios still faces two challenges: high-quality data scarcity and high computational demands. This paper proposes EThai-ASR, the first to apply large language models (LLMs) to Thai ASR and create an efficient LLM-based ASR system. EThai-ASR comprises a speech encoder, a connection module and a Thai LLM decoder. To address the data scarcity and obtain a powerful speech encoder, EThai-ASR introduces a self-evolving data refinement strategy to refine weak labels, yielding an enhanced speech encoder. Moreover, we propose a pluggable sequence compression module used in the connection module with three modes designed to reduce the sequence length, thus decreasing computational demands while maintaining decent performance. Extensive experiments demonstrate that EThai-ASR has achieved state-of-the-art accuracy in multiple datasets. We release our refined text transcripts to promote further research.
Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languages
Authors: Tuan Nguyen; Huy Dat Tran
Venue: Interspeech 2025 (main)

Abstract: Code-switching (CS), common in multilingual settings, presents challenges for ASR due to scarce and costly transcribed data caused by linguistic complexity. This study investigates building CS-ASR using synthetic CS data. We propose a phrase-level mixing method to generate synthetic CS data that mimics natural patterns. Utilizing monolingual augmented with synthetic phrase-mixed CS data to fine-tune large pretrained ASR models (Whisper, MMS, SeamlessM4T). This paper focuses on three under-resourced Southeast Asian language pairs: Malay-English (BM-EN), Mandarin-Malay (ZH-BM), and Tamil-English (TA-EN), establishing a new comprehensive benchmark for CS-ASR to evaluate the performance of leading ASR models. Experimental results show that the proposed training strategy enhances ASR performance on monolingual and CS tests, with BM-EN showing highest gains, then TA-EN and ZH-BM. This finding offers a cost-effective approach for CS-ASR development, benefiting research and industry.
Swedish Whispers; Leveraging a Massive Speech Corpus for Swedish Speech Recognition
Authors: Leonora Vesterbacka; Faton Rekathati; Robin Kurtz; Justyna Sikora; Agnes ToftgÃ¥rd
Venue: Interspeech 2025 (main)

Abstract: This work presents a suite of fine-tuned Whisper models for Swedish, trained on a dataset of unprecedented size and variability for this mid-resourced language. As languages of smaller sizes are often underrepresented in multilingual training datasets, substantial improvements in performance can be achieved by fine-tuning existing multilingual models, as shown in this work. This work reports an overall improvement across model sizes compared to OpenAI's Whisper evaluated on Swedish. Most notably, we report an average 47% reduction in WER comparing our best performing model to OpenAI's Whisper-large-v3, in evaluations across FLEURS, Common Voice, and NST.
Room Impulse Response as a Prompt for Acoustic Echo Cancellation
Authors: Fei Zhao; Shulin He; Xueliang Zhang
Venue: Interspeech 2025 (main)

Abstract: Data-driven acoustic echo cancellation (AEC) methods, predominantly trained on synthetic or constrained real-world datasets, encounter performance declines in unseen echo scenarios, especially in real environments where echo paths are not directly observable. Our proposed method counters this limitation by integrating room impulse response (RIR) as a pivotal training prompt, aiming to improve the generalization of AEC models in such unforeseen conditions. We also explore four RIR prompt fusion methods. Comprehensive evaluations, including both simulated RIR under unknown conditions and recorded RIR in real, demonstrate that the proposed approach significantly improves performance compared to baseline models. These results substantiate the effectiveness of our RIR-guided approach in strengthening the model's generalization capabilities.