Speaker Adaptive Training of Deep Neural Network Acoustic Models Using I-Vectors

Abstract

In acoustic modeling, speaker adaptive training (SAT) has been a long-standing technique for the traditional Gaussian mixture models (GMMs). Acoustic models trained with SAT become independent of training speakers and generalize better to unseen testing speakers. This paper ports the idea of SAT to deep neural networks (DNNs), and proposes a framework to perform feature-space SAT for DNNs. Using i-vectors as speaker representations, our framework learns an adaptation neural network to derive speaker-normalized features. Speaker adaptive models are obtained by fine-tuning DNNs in such a feature space. This framework can be applied to various feature types and network structures, posing a very general SAT solution. In this paper, we fully investigate how to build SAT-DNN models effectively and efficiently. First, we study the optimal configurations of SAT-DNNs for large-scale acoustic modeling tasks. Then, after presenting detailed comparisons between SAT-DNNs and the existing DNN adaptation methods, we propose to combine SAT-DNNs and model-space DNN adaptation during decoding. Finally, to accelerate learning of SAT-DNNs, a simple yet effective strategy, frame skipping, is employed to reduce the size of training data. Our experiments show that compared with a strong DNN baseline, the SAT-DNN model achieves 13.5% and 17.5% relative improvement on word error rates (WERs), without and with model-space adaptation applied respectively. Data reduction based on frame skipping results in 2 $ \times $ speed-up for SAT-DNN training, while causing negligible WER loss on the testing data.