Whisper-Island Detection Based on Unsupervised Segmentation With Entropy-Based Speech Feature Processing

Whisper island detection is a challenging research problem which has received little attention in the research community. Effective whisper-island detection is the first step necessary to ensure engagement of effective subsequent speech processing steps to address mismatch between whisper and neutral speech production. In this paper, we propose an effective approach for detecting whisper-islands embedded within normally phonated speech via BIC/T²-BIC using a proposed 4-D feature set. Performance is assessed using our proposed multi-error score (MES), which shows that the new proposed algorithm achieves the lowest MES (11.51) to date and along with a perfect 100% correct whisper/neutral vocal effort labeling. The results show that we can correctly and precisely detect vocal effort change points (VECP) between whisper-islands and neutral speech as well as label the vocal effort of the whisper-island. The proposed feature is sensitive to the vocal effort change between whisper and neutral speech and is gender independent. The result suggests that the proposed algorithm is effective and precise for the whisper-island detection.