Segment-removal based stuttered speech remediation

Abstract

Speech remediation by identifying those segments which take away from the substance of the speech content can be performed by correctly identifying portions of speech which can be deleted without diminishing from the speech quality, but rather improving the speech. Speech remediation is especially important when the speech is disfluent as in the case of stuttered speech. In this paper, we describe a stuttered speech remediation approach based on the identification of those segments of speech which when removed would enhance speech understandability in terms of both speech content and speech flow. The approach we adopted consists of first identifying and extracting speech segments that have weak significance due to their low relative intensity, then classifying the segments that should be removed. We trained several classifiers using a large set of inherent and derived features extracted from the audio segments for the purpose of automatic improvement of stuttered speech by providing a second layer filtering stage. This second layer would discern the audio segments that need to be eliminated from the ones that do not. The resulting speech is then compared to the manually-labeled “gold standard” optimal speech. The quality comparisons of the resulting enhanced speeches and their manually-labeled counterparts were favorable and the corresponding tabulated results are presented below. To further enhance the quality of the classifiers we adopted a voting techniques that encompassed an extended set of models from 14 algorithms and presented the classifier performance measures from different voting threshold values. This voting approach allowed us to improve the specificity of the classification by reducing the false positive classifications at the expense on additional false negatives thus improving the practical effectiveness of the system.