Dysfluency Classification in Stuttered Speech Using Deep Learning for Real-Time Applications
Title | Dysfluency Classification in Stuttered Speech Using Deep Learning for Real-Time Applications |
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Author | |
Abstract | Stuttering detection and classification are important issues in speech therapy as they could help therapists track the progression of patients’ dysfluencies. This is also an important tool for technology-assisted speech therapy. In this paper, we combine MFCC and phoneme probabilities to train a neural network for stuttering detection and classification of four dysfluency types. We evaluate our system on the UCLASS, FluencyBank and SEP-28K datasets and show that our system is effective and suitable for real-time applications. |
Year of Publication |
2022
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Conference Name |
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Date Published |
May
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DOI |
10.1109/ICASSP43922.2022.9746638
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