HMM-based whisper recognition using $\mu$-law frequency warping

Due to the lack of sufficient amount of whisper data for training, whispered speech recognition is a serious challenge for state-of-the-art Automatic Speech Recognition (ASR) systems. Because of great acoustic mismatch between neutral and whispered speech, ASR systems are faced with significant drop of performance when applied to whisper.
In this paper, we give an analysis of neutral and whispered speech recognition based on traditional Hidden Markov Models (HMM) framework, in a Speaker Dependent (SD) and Speaker Independent (SI) cases. Special attention is paid to the neutral-trained recognition of whispered speech (N/W scenario). The ASR system is developed for recognition of isolated words from a real database (Whi-Spe) of neutral-whisper speech pairs. In the N/W scenario, a meaningful gain in robustness is achieved with the proposed frequency warping, originally developed for speech signal compression and expanding in digital telecommunication systems. Simultaneously, good performances in recognition of neutral speech are retained.
Compared to baseline recognition with Mel-frequency Cepstral Coefficients (MFCC), word recognition accuracy with cepstral coefficients using proposed frequency warping (denoted as $\mu$FCC) is improved for 7.36% (SD) and 3.44% (SI), absolute. As well, the $\mathrm{F}$-measure (harmonic mean of the precission and recall) for $\mu$FCC feature vectors is increased for 6.90% (SD) and 3.59 (SI). Statistical tests confirm significance of the achieved improvement in recognition accuracy.