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FiLM technique enhances ASR for pathological speech

Researchers have developed a new method for improving automatic speech recognition (ASR) for pathological speech using a technique called Feature-wise Linear Modulation (FiLM). This approach injects speaker-specific information into a frozen ASR encoder, allowing it to adapt to individual speakers without altering the base model's weights. The method was benchmarked against existing adaptation strategies on Spanish and English pathological speech datasets, showing competitive performance while maintaining the ability to answer speech-related questions. AI

IMPACT This research could lead to more accurate speech recognition for individuals with neurological conditions, improving accessibility and communication tools.

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

Read on arXiv cs.CL →

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

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Fernando L\'opez, Santosh Kesiraju, Jordi Luque ·

    FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition

    arXiv:2606.06211v1 Announce Type: new Abstract: Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a significant challenge. We investigate speaker conditioning via Feature-wise Linear M…

  2. arXiv cs.CL TIER_1 English(EN) · Jordi Luque ·

    FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition

    Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a significant challenge. We investigate speaker conditioning via Feature-wise Linear Modulation (FiLM), injecting x-vector-derived inf…