Space-and-speaker-aware acoustic modeling with effective data augmentation for recognition of multi-array conversational speech

Abstract

We propose a space-and-speaker-aware (SSA) approach to acoustic modeling (AM), denoted as SSA-AM, to improve system performances of automatic speech recognition (ASR) in distant multi-array conversational scenarios. In contrast to conventional AM which only uses spectral features from a target speaker as inputs, the inputs to SSA-AM consists of speech features from both the target and interfering speakers, which contain discriminative information from different speakers, including spatial information embedded in interaural phase differences (IPDs) between individual interfering speakers and the target speaker. In the proposed SSA-AM framework, we explore four acoustic model architectures consisting of different combinations of four neural networks, namely deep residual network, factorized time delay neural network, self-attention and residual bidirectional long short-term memory neural network. Various data augmentation techniques are adopted to expand the training data to include different options of beamformed speech obtained from multi-channel speech enhancement. Evaluated on the recent CHiME-6 Challenge Track 1, our proposed SSA-AM framework achieves consistent recognition performance improvements when compared with the official baseline acoustic models. Furthermore, SSA-AM outperforms acoustic models without explicitly using the space and speaker information. Finally, our data augmentation schemes are shown to be especially effective for compact model designs. Code is released at https://github.com/coalboss/SSA_AM.