Automatic detection of prolongations and repetitions using LPCC

Abstract

Stuttering is a speech disorder in which the normal flow of speech is disrupted by occurrences of dysfluencies, such as repetitions, interjection and so on. There are high proportion of repetitions and prolongations in stuttered speech, usually at the beginning of sentences. Consequently, acoustic analysis can be used to classify the stuttered events. This paper describes particular stuttering events to be located as repetitions and prolongations in stuttered speech with feature extraction algorithm. Linear predictive cepstral coefficient (LPCC) feature extraction is implemented to test its effectiveness in recognizing prolongations and repetitions in stuttered speech. In this work, two classifiers, linear discriminant analysis classifier (LDA) and k-nearest neighbors (k-NN) are employed. Result shows that the LPCC and classifier (LDA and k-NN) can be used for the recognition of repetitions and prolongations in stuttered speech with the best accuracy of 89.77%.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 59 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.