Rapid Speaker Adaptation of Neural Network Based Filterbank Layer for Automatic Speech Recognition

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Deep neural networks (DNN) have achieved significant success in the field of automatic speech recognition. Previously, we proposed a filterbank-incorporated DNN which takes power spectra as input features. This method has a function of VTLN (Vocal tract length normalization) and fMLLR (feature-space maximum likelihood linear regression). The filterbank layer can be implemented by using a small number of parameters and is optimized under a framework of backpropagation. Therefore, it is advantageous in adaptation under limited available data. In this paper, speaker adaptation is applied to the filterbank-incorporated DNN. By applying speaker adaptation using 15 utterances, the adapted model gave a 7.4% relative improvement in WER over the baseline DNN at a significance level of 0.005 on CSJ task. Adaptation of filterbank layer also showed better performance than the other adaptation methods; singular value decomposition (SVD) based adaptation and learning hidden unit contributions (LHUC).

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