2021 29th European Signal Processing Conference (EUSIPCO) 2021
DOI: 10.23919/eusipco54536.2021.9616063
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StutterNet: Stuttering Detection Using Time Delay Neural Network

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Cited by 26 publications
(32 citation statements)
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“…This study is an extension of our previous work Stutter-Net [16]. Due to the diversity and uniqueness of stuttering, it continues to be the most demanding and challenging to detect, due to its inherent nature of huge class imbalance across different types of disfluencies [2].…”
Section: B Related Workmentioning
confidence: 90%
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“…This study is an extension of our previous work Stutter-Net [16]. Due to the diversity and uniqueness of stuttering, it continues to be the most demanding and challenging to detect, due to its inherent nature of huge class imbalance across different types of disfluencies [2].…”
Section: B Related Workmentioning
confidence: 90%
“…The networks were trained using the spectrograms, which were the sole input features to the model. The proposed architecture showed promising results on a small subset of speakers (25) of UCLASS dataset, but did not perform well on a large set of speakers [16]. In addition, they have mentioned it end-to-end, but they are still using the hand crafted spectrogram features.…”
Section: B Related Workmentioning
confidence: 99%
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