Generative modeling of pseudo-target domain adaptation samples for whispered speech recognition

The lack of available large corpora of transcribed whispered speech is one of the major roadblocks for development of successful whisper recognition engines. Our recent study has introduced a Vector Taylor Series (VTS) approach to pseudo-whisper sample generation which requires availability of only a small number of real whispered utterances to produce large amounts of whisper-like samples from easily accessible transcribed neutral recordings. The pseudo-whisper samples were found particularly effective in adapting a neutral-trained recognizer to whisper. Our current study explores the use of denoising autoencoders (DAE) for pseudo-whisper sample generation. Two types of generative models are investigated - one which produces pseudo-whispered cepstral vectors on a frame basis and another which generates pseudo-whisper statistics of whole phone segments. It is shown that the DAE approach considerably reduces word error rates of the baseline system as well as the system adapted on real whisper samples. The DAE approach provides competitive results to the VTS-based method while cutting its computational overhead nearly in half.