The Vietnamese speech recognition based on rectified linear units deep neural network and spoken term detection system combination

In this paper, we report our recent progress on the under-resource language automatic speech recognition (ASR) and the following spoken term detection (STD). The experiments are carried on the National Institute of Standards and Technology (NIST) Open Keyword Search 2013 (OpenKWS13) evaluation Vietnamese corpus. Compared with the conventional ASR system, we made the following modifications to improve recognition accuracy. First, pitch features and tone modeling are applied to cover pitch and tone information since Vietnamese is a tonal language. Second, automatic question generation for decision tree is used for state tying to address the problem of lack of linguistic knowledge. Finally, we investigate rectified linear units (ReLUs) activation function and cross-lingual pre-training in deep neural network (DNN) acoustic model training. In the STD procedure, we adopt term-dependent score normalization and combine the outputs of diverse ASR systems to increase actual term weighted value (ATWV). After applying these methods, our current best single system achieves 48.32% word accuracy and 0.398 ATWV after STD system combination on OpenKWS13 Vietnamese development set.