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New ASR Method Learns to Process Speech Disfluencies with Continual Learning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new approach to improve Automatic Speech Recognition (ASR) systems by incorporating continual learning techniques to better handle disfluent speech. The method involves introducing explicit disfluency tokens into pre-trained ASR models and then fine-tuning them on diverse datasets. This process aims to prevent catastrophic forgetting of general knowledge while enhancing the model's ability to recognize and process speech disfluencies, addressing a key challenge in current ASR technology. AI

IMPACT This research could lead to more robust ASR systems capable of handling natural, unscripted speech, improving user experience in voice-enabled applications.

RANK_REASON The cluster contains an academic paper detailing a new research methodology for ASR.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Henri-Leon Kordt, Theresa Pekarek Rosin, Jae Hee Lee, Stefan Wermter ·

    Learning to Hear Hesitation: Continual Learning for Disfluency-Aware ASR

    arXiv:2606.14391v1 Announce Type: cross Abstract: Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, leading to information loss and hallucinations. Prior …

  2. arXiv cs.AI TIER_1 English(EN) · Stefan Wermter ·

    Learning to Hear Hesitation: Continual Learning for Disfluency-Aware ASR

    Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, leading to information loss and hallucinations. Prior work has focused on verbatim transcription and the…

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