Analysis of Machine Learning Methods for Speech Disfluencies’ Classification

Author:

Sharma Nitin Mohan,Mahapatra Prasant Kumar,Gandhi Vaibhav

Publisher

Springer Nature Singapore

Reference38 articles.

1. Esmaili I, Dabanloo NJ, Vali M (2016) Automatic classification of speech dysfluencies in continuous speech based on similarity measures and morphological image processing tools. Biomed Signal Process Control 23:104–114

2. Bloodstein O (1969) A handbook on stuttering

3. Awad SS (1997) The application of digital speech processing to stuttering therapy. In: IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings. IEEE

4. Chee LS, Ai OC, Yaacob S (2009) Overview of automatic stuttering recognition system. In: Proceedings on International Conference on Man-Machine Systems, no. October, Batu Ferringhi, Penang Malaysia

5. Chang S-E, Erickson KI, Ambrose NG, Hasegawa-Johnson MA, Ludlow CL (2008) Brain anatomy differences in childhood stuttering. Neuroimage 39(3):1333–1344

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