@inproceedings{13,
  author = {Melanie Jouaiti and Kerstin Dautenhahn},
  title = {Dysfluency Classification in Stuttered Speech Using Deep Learning for Real-Time Applications},
  abstract = {<p>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.</p>
},
  year = {2022},
  journal = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages = {6482-6486},
  month = {May},
  issn = {2379-190X},
  doi = {10.1109/ICASSP43922.2022.9746638},
}