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Stuttering is a speech impediment affecting tens of millions of people on aneveryday basis. Even with its commonality, there is minimal data and researchon the identification and classification of stuttered speech. This papertackles the problem of detection and classification of different forms ofstutter. As opposed to most existing works that identify stutters with languagemodels, our work proposes a model that relies solely on acoustic features,allowing for identification of several variations of stutter disfluencieswithout the need for speech recognition. Our model uses a deep residual networkand bidirectional long short-term memory layers to classify different types ofstutters and achieves an average miss rate of 10.03%, outperforming thestate-of-the-art by almost 27%

Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memory