Incorporating Context Information into Deep Neural Network Acoustic Models

The introduction of deep neural networks (DNNs) has advanced the performance of automatic speech recognition (ASR) tremendously. On a wide range of ASR tasks, DNN models show superior performance than the traditional Gaussian mix-ture models (GMMs). Although making significant advances, DNN models still suffer from data scarcity, speaker mismatch and environment variability. This thesis resolves these challenges by fully exploiting DNNs ’ ability of integrating heteroge-neous features under the same optimization objective. We propose to improve DNN models under these challenging conditions by incorporating context information into DNN training. On a new language, the amount of training data may become highly limited. This data scarcity causes degradation on the recognition accuracy of DNN models. A solution is to transfer knowledge from other languages to the low-resource condi-tion. This thesis proposes a framework to build cross-language DNNs via language-universal feature extractors (LUFEs). Convolutional neural networks (CNNs) an