  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.
