An investigation of augmenting speaker representations to improve speaker normalisation for DNN-based speech recognition

Abstract

The conventional short-term interval features used by the Deep Neural Networks (DNNs) lack the ability to learn longer term information. This poses a challenge for training a speaker-independent (SI) DNN since the short-term features do not provide sufficient information for the DNN to estimate the real robust factors of speaker-level variations. The key to this problem is to obtain a sufficiently robust and informative speaker representation. This paper compares several speaker representations. Firstly, a DNN speaker classifier is used to extract the bottleneck features as the speaker representation, called the Bottleneck Speaker Vector (BSV). To further improve the robustness of this representation, a first-order Bottleneck Speaker Super Vector (BSSV) is also proposed, where the BSV is expanded into a super vector space by incorporating the phoneme posterior probabilities. Finally, a more fine-grain speaker representation based on the FMLLR-shifted features is examined. The experimental results on the WSJ0 and WSJ1 datasets show that the proposed speaker representations are useful in normalising the speaker effects for robust DNN-based automatic speech recognition. The best performance is achieved by augmenting both the BSSV and the FMLLR-shifted representations, yielding 10.0% - 15.3% relatively performance gains over the SI DNN baseline.