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StutterNet: Stuttering Detection Using Time Delay Neural Network

Shakeel Ahmad Sheikh 1 Md Sahidullah 1 Fabrice Hirsch 2 Slim Ouni 1
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : This paper introduces StutterNet, a novel deep learning based stuttering detection capable of detecting and identifying various types of disfluencies. Most of the existing work in this domain uses automatic speech recognition (ASR) combined with language models for stuttering detection. Compared to the existing work, which depends on the ASR module, our method relies solely on the acoustic signal. We use a time-delay neural network (TDNN) suitable for capturing contextual aspects of the disfluent utterances. We evaluate our system on the UCLASS stuttering dataset consisting of more than 100 speakers. Our method achieves promising results and outperforms the state-of-the-art residual neural network based method. The number of trainable parameters of the proposed method is also substantially less due to the parameter sharing scheme of TDNN.
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https://hal.inria.fr/hal-03227223
Contributor : Shakeel Ahmad Sheikh Connect in order to contact the contributor
Submitted on : Monday, May 17, 2021 - 10:02:49 AM
Last modification on : Saturday, October 16, 2021 - 11:26:10 AM
Long-term archiving on: : Wednesday, August 18, 2021 - 6:13:43 PM

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  • HAL Id : hal-03227223, version 1
  • ARXIV : 2105.05599

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Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch, Slim Ouni. StutterNet: Stuttering Detection Using Time Delay Neural Network. EUSIPCO 2021 – 29th European Signal Processing Conference, Aug 2021, Dublin/ Virtual, Ireland. ⟨hal-03227223⟩

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