High accuracy transcription, cleaning and fast term detection for spoken documents

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We studied on automatic speech recognition, written style transformation from spoken style transcription and fast term detection for spoken documents. For accurately automatic speech recognition, we proposed a novel speech recognition model beyond the conventional HMM, and showed the effectiveness. For cleaning speech recognition results, we proposed a probabilistic model of cleaning from spoken style with recognition errors to written style and showed the effectiveness. Finally, we proposed a fast term detection method based on tri-gram indexes from the transcription of spoken documents and showed the effectiveness.