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investigation of self -supervised speech models for stuttered speech detection

  • Unique Paper ID: 182048
  • Volume: 12
  • Issue: 2
  • PageNo: 520-526
  • Abstract:
  • A speech condition called stuttering is typified by irregularities in speech fluency, such as repetitions, blocks, and prolongations. Speech-language pathologists' (SLPs') manual evaluations, which take a lot of time and need specialized knowledge, are a major component of traditional diagnosis. This study explores utterance-level stuttering detection using self-supervised learning (SSL) models to facilitate automated evaluation. We specifically assess how well a number of pretrained SSL speech models perform on utterance-level stuttering categorization tasks: WavLM Base, HuBERT Base, Wav2Vec 2.0 Base, WavLM Large, HuBERT Large, and Wav2Vec 2.0 Large. The Kassel State of Fluency (KSoF) dataset, FluencyBank, and SEP-28K are used for independent testing, and the models are refined using these datasets. F1 scores for various stuttering types are used to gauge performance. All three test sets (SEP-28K, FluencyBank, and KSoF) have the following F1 values: WavLM Base (0.797, 0.800, 0.772), HuBERT Base (0.790, 0.790, 0.766), Wav2Vec 2.0 Base (0.778, 0.782, 0.758), WavLM Large (0.832, 0.832, 0.758), HuBERT Large (0.817, 0.816, 0.788), and Wav2Vec 2.0 Large (0.804, 0.803, 0.779).WavLM Large continuously performs the best on utterance-level benchmarks out of all the models. This comparison study demonstrates how well SSL models identify stuttering and offers information about how they may be used in actual speech pathology and fluency disorder evaluation.
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  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 2
  • PageNo: 520-526

investigation of self -supervised speech models for stuttered speech detection

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