StutterNet: Stuttering Detection Using Time Delay Neural Network
This addresses stuttering detection for speech analysis, offering an incremental improvement by eliminating reliance on automatic speech recognition.
The paper tackled stuttering detection by proposing StutterNet, a deep learning method that uses only acoustic signals with a time-delay neural network, achieving promising results and outperforming state-of-the-art methods on a dataset of over 100 speakers.
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.