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This paper describes our automatic speech recognition system for IWSLT2014 evaluation campaign. The system is based on weighted finite-state transducers and a combination of multiple subsystems which consists of four types of acoustic feature sets, four types of acoustic models, and Ngram and recurrent neural network language models. Compared with our(More)
Tone plays an important role in distinguishing lexical meaning in tonal languages, such as Mandarin and Thai. It has been revealed that tone information is helpful to improve automatic speech recognition (ASR) for these languages. In this study, we incorporate tone features from the fundamental frequency (Fo) and fundamental frequency variation (FFV) to the(More)
We propose a method to adapt acoustic models for robust speech recognition in real environments using data from other languages. In real-world speech recognition systems, we can effectively adapt acoustic models using the speech data logged by the system. However, when developing a system for a new language, this step is impossible since we have no such(More)
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