Semi-Supervised Speaker Adaptation for In-Vehicle Speech Recognition with Deep Neural Networks

In this paper, we present a new i-vector based speaker adaptation method for automatic speech recognition with deep neural networks, focusing on in-vehicle scenarios. Our proposed method is, rather than augmenting i-vectors to acoustic feature vectors to form concatenated input vectors for adapting neural network acoustic model parameters, is to perform featurespace transformation with smaller transformation neural networks dedicated to acoustic feature vectors and i-vectors, respectively, followed by a layer of linear combination of the network outputs. This feature-space transformation is learned via semi-supervised learning without any parameter change in the original deep neural network acoustic model. Experimental results show that our proposed method achieves 18.3% relative improvement in terms of word error rate compared to the speaker independent performance, and verify that it has a potential to replace well-known feature-space Maximum Likelihood Linear Regression (fMLLR) in in-vehicle speech recognition with deep neural networks.