Machine Translation of Spontaneous Speech

Recent advances in machine translation have promoted extensive development of many applications. When applied to spontaneous speech input, however, their performance is severely degraded due to the distinctive characteristics of spontaneous speech. In this thesis, machine learning techniques to model the characteristics are developed. It was shown that transforming the spontaneous speech into well-written sentences is crucial in order to improve the machine translation of spontaneous speech.