  This paper presents a new approach for classification of dysfluent and fluent
speech using Mel-Frequency Cepstral Coefficient (MFCC). The speech is fluent
when person's speech flows easily and smoothly. Sounds combine into syllable,
syllables mix together into words and words link into sentences with little
effort. When someone's speech is dysfluent, it is irregular and does not flow
effortlessly. Therefore, a dysfluency is a break in the smooth, meaningful flow
of speech. Stuttering is one such disorder in which the fluent flow of speech
is disrupted by occurrences of dysfluencies such as repetitions, prolongations,
interjections and so on. In this work we have considered three types of
dysfluencies such as repetition, prolongation and interjection to characterize
dysfluent speech. After obtaining dysfluent and fluent speech, the speech
signals are analyzed in order to extract MFCC features. The k-Nearest Neighbor
(k-NN) and Support Vector Machine (SVM) classifiers are used to classify the
speech as dysfluent and fluent speech. The 80% of the data is used for training
and 20% for testing. The average accuracy of 86.67% and 93.34% is obtained for
dysfluent and fluent speech respectively.
