Whispered Speech Recognition using Hidden Markov Models and Support Vector Machines 

Abstract

Whisper is a specific mode of speech characterized by turbulent airflow at the glottis level. Despite an increased effort in speech perception, the intelligibility of whisper in human communication is very high. An enormous acoustic mismatch between normally phonated (neutral) and whispered speech is the main reason why modern Automatic Speech Recognition (ASR) systems have significant drop of performances when applied to whisper. In this paper, we present an analysis in recognition of whisper using 2 machinelearning techniques: Hidden Markov Models (HMM) and Support Vector Machines (SVM). The experiments are conducted in both Speaker Dependent (SD) and Speaker Independent (SI) fashion for Whi-Spe speech database. The best neutral-trained whisper recognition accuracy in SD fashion (83.36%) is obtained in SVM framework. At the same time, HMMbased recognition gave the highest recognition accuracy in SI fashion (87.42%). The results in recognition of neutral speech are given as well.

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“…The final accuracy reached 96.39% (Whi-Spe), 90.61% (GEES), and 84.34% (DB1). Similar trends between HMM and SVM recognition were observed in experiments involving speaker-dependent cases [31].…”

“…Research studies, such as the one presented in [30], have shown better results in SVM recognition of isolated words using the first approach. The best results in recognizing words from the Whi-Spe database (in the speaker-dependent case) were achieved using 18 windows per utterance [31]. Therefore, in our experiments, segmentation into 18 overlapping windows was employed.…”