SEQUENTIAL K-NEAREST NEIGHBOR PATTERN RECOGNITION FOR USABLE SPEECH CLASSIFICATION (WedAmOR6)

Author(s) : Jashmin Shah (SPEECH PROCESSING LAB, TEMPLE UNIVERSITY, USA) Brett Smolenski (SPEECH PROCESSING LAB, TEMPLE UNIVERSITY, USA) Robert Yantorno (SPEECH PROCESSING LAB, TEMPLE UNIVERSITY, USA) Ananth Iyer (THE PENNSYLVANIA STATE UNIVERSITY, USA)

Abstract : The accuracy of speech processing techniques degrades when operating in a co-channel environment. Co-channel speech occurs when more than one person is talking at the same time. The idea of usable speech segmentation is to identify and extract those portions of co-channel speech that are minimally degraded but still useful for speech processing application such as speaker identification. Usable speech measures are features that are extracted from the co-channel signal to distinguish between usable and unusable speech. In this paper, a new usable speech extraction technique is presented. The new method extracts features recursively and variable length segmentation is performed by making sequential decisions on the k-NN pattern classifier class assignments. This new approach is able to identify 79% of available usable speech segments with 21% false alarms and it requires lesser amount of data to make accurate decisions compared to previously presented methods.

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