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New research advances ASR for dysarthric speech and synthetic data use · 4 sources tracked

By PulseAugur Editorial · [6 sources] ·

Researchers are exploring new methods to improve automatic speech recognition (ASR) systems. One study details how fine-tuning the Whisper model with personalized data significantly reduced word error rates for dysarthric speech, achieving a 9.7% error rate with extensive data. Another paper investigates the use of synthetic speech for training ASR systems, finding that augmenting synthetic audio with room impulse responses can bridge the gap with real-world data. Additionally, a new test set called PreferenceASR has been developed to evaluate ASR systems based on their ability to follow user-specified output preferences, revealing performance differences obscured by traditional benchmarks. AI

IMPACT Advances in ASR personalization and synthetic data utilization could broaden access to speech technologies for diverse user groups.

RANK_REASON The cluster consists of multiple academic papers published on arXiv detailing research into ASR systems.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New research advances ASR for dysarthric speech and synthetic data use · 4 sources tracked

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COVERAGE [6]

  1. arXiv cs.CL TIER_1 English(EN) · Ruchao Fan, Yiming Wang, Rui Zhao, Liliang Ren, Keqi Deng, Xiaoyang Chen, Ali Zare, Bo Ren, Yuxuan Hu, Junkun Chen, Yan Huang, Yelong Shen, Jinyu Li ·

    Rethinking Speech-LLM Integration for ASR: Effective Joint Speech-Text Training by Interleaving

    arXiv:2607.01733v1 Announce Type: new Abstract: Speech-LLM integration has shown promising results by leveraging extensive textual pretraining, yet its specific benefits for automatic speech recognition (ASR) remain unclear. We observe that as supervised ASR training data increas…

  2. arXiv cs.CL TIER_1 English(EN) · Jinyu Li ·

    Rethinking Speech-LLM Integration for ASR: Effective Joint Speech-Text Training by Interleaving

    Speech-LLM integration has shown promising results by leveraging extensive textual pretraining, yet its specific benefits for automatic speech recognition (ASR) remain unclear. We observe that as supervised ASR training data increases, the contribution of LLM priors becomes less …

  3. arXiv cs.CL TIER_1 English(EN) · Christian Huber, Laura Kernahan, Alexander Waibel ·

    Adapting Foundation ASR Models to Dysarthric Speech: A Case Study

    arXiv:2606.31722v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often perform poorly in dysarthric speech, limiting their usefulness to affected speakers in everyday communication. This paper presents a personalized ASR system for a dysarthric speaker, …

  4. arXiv cs.CL TIER_1 English(EN) · Alexander Waibel ·

    Adapting Foundation ASR Models to Dysarthric Speech: A Case Study

    Automatic speech recognition (ASR) systems often perform poorly in dysarthric speech, limiting their usefulness to affected speakers in everyday communication. This paper presents a personalized ASR system for a dysarthric speaker, built by adapting a foundation ASR model to spea…

  5. arXiv cs.AI TIER_1 English(EN) · Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso, S\'everin Baroudi, Shashi Kumar, Esa\'u Villatoro-Tello, Srikanth Madikeri, Manjunath K E, Old\v{r}ich Plchot, Kadri Hacio\u{g}lu, Petr Motlicek, Andreas Stolcke ·

    How to Leverage Synthetic Speech for LLM-Based ASR Systems?

    arXiv:2606.29031v1 Announce Type: cross Abstract: In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is an appealing alternative for training automatic spe…

  6. arXiv cs.CL TIER_1 English(EN) · Nithin Rao Koluguri, Sasha Meister, Nikolay Karpov, Piotr Zelasko, Desh Raj, Jagadeesh Balam, Boris Ginsburg ·

    Preference-ASR: A Preference-Aware Test Set for Benchmarking ASR in the Era of Speech LLMs

    arXiv:2606.29534v1 Announce Type: new Abstract: Popular ASR test sets adopt inconsistent conventions for numbers, disfluencies, entities, and casing, while standard normalizers erase the format distinctions users care about. Current benchmarks therefore cannot measure whether a m…

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