Model and feature based compensation for whispered speech recognition
Shabnam Ghaffarzadegan, Hynek Bořil, John H. L. Hansen · 2014
This study proposes model and feature based strategies for au-tomatic whispered speech recognition. Our goal is to compensate for the mismatch between neutral-trained recognizer models and parameters of whispered speech. We propose a pseudo-whisper generation from neutral speech samples for efficient acoustic model adaptation. The scheme is based on the popular Vector Tay-lor Series (VTS) algorithm. In the first step, a ‘background ’ model capturing a rough estimate of the target whispered speech charac-teristics from a small amount of whispered data is trained. Second, the target background model is utilized in the VTS strategy to es-tablish broad phone classes (consonants and vowels) transforma-tions for individual neutral utterances and transform them towards whisper. Finally, these pseudo-whisper samples are used to adapt neutral recognizer models towards whisper. This approach is eval-uated together with Vocal Tract Length Normalization (VTLN) and Shift frequency transforms and show to greatly benefit recog-nition performance compared to a traditional whisper-adaptation approach. The absolute WER on the closed speakers whisper sce-nario has been reduced from 17.3 % to 8.4 % and the open speakers scenario from 27.7 % to 17.5 %. Index Terms: whispered speech recognition, Vector Taylor Series, vocal length normalization