  Stuttering is a speech impediment affecting tens of millions of people on an
everyday basis. Even with its commonality, there is minimal data and research
on the identification and classification of stuttered speech. This paper
tackles the problem of detection and classification of different forms of
stutter. As opposed to most existing works that identify stutters with language
models, our work proposes a model that relies solely on acoustic features,
allowing for identification of several variations of stutter disfluencies
without the need for speech recognition. Our model uses a deep residual network
and bidirectional long short-term memory layers to classify different types of
stutters and achieves an average miss rate of 10.03%, outperforming the
state-of-the-art by almost 27%
