@inproceedings{30766a1b983046319e242d24d7bc3fde,
title = "Dysfluency Classification in Stuttered Speech Using Deep Learning for Real-Time Applications",
abstract = "Stuttering detection and classification are important issues in speech therapy as they could help therapists track the progression of patients{\textquoteright} 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.",
keywords = "Deep learning, Conferences, Neural networks, Medical treatment, Signal processing, Real-time systems, Acoustics",
author = "Melanie Jouaiti and Kerstin Dautenhahn",
note = "Presented 27 May 2022, at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; Conference date: 23-05-2022 Through 27-05-2022",
year = "2022",
month = apr,
day = "27",
doi = "10.1109/ICASSP43922.2022.9746638",
language = "English",
isbn = "9781665405416",
series = "International Conference on Acoustics, Speech, and Signal Processing (ICASSP)",
publisher = "IEEE",
pages = "6482--6486",
booktitle = "ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
}