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New forced alignment method boosts accuracy for noisy spontaneous speech

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for forced alignment of spontaneous speech that significantly improves accuracy in noisy conditions. By incorporating explicit units for fillers and non-lexical vocal events, and using upsampled WavLM features with a discriminative projection, the system achieves lower word-boundary Mean Absolute Errors (MAEs) on conversational and disfluent speech datasets compared to existing baselines. This approach also drastically reduces catastrophic misalignments, making it more robust than current leading aligners. AI

IMPACT Improves accuracy for speech recognition and analysis in real-world, noisy conditions.

RANK_REASON The cluster contains an academic paper detailing a new method for speech processing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New forced alignment method boosts accuracy for noisy spontaneous speech

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The cluster contains an academic paper detailing a new method for speech processing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Laurin Wagner, Mario Zusag ·

    Noise-Robust Forced Alignment for Spontaneous Speech

    arXiv:2610.22583v1 Announce Type: new Abstract: We show that forced aligners degrade on spontaneous, noisy real-world speech, and that fillers, laughter, and disfluencies corrupt word boundaries, impacting many downstream applications. We revisit the classical GMM-HMM aligner wit…

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