A design and implementation of HMM based Mongolian speech recognition system

In this paper, we describe the design and development of HMM-based speech recognition system for the Mongolian language. Mongolian language is one of the with low resources languages for speech processing area. To build a Large Vocabulary Continuous Speech Recognition (LVCSR) system, high accurate acoustic models and large-scale language models are essential. There were no Mongolian speech database and text corpus for use in study. First, we collected text corpus. The text is selected from television programs, newspapers and web. Selection criterion was to cover as many different subjects as possible. In speech data, the most frequent words are selected from the text corpus. We are training the acoustic and language models based on Hidden Markov Models (HMMs). We evaluated the performance of isolated word recognition with context independent and context dependent models.