In this paper we document our experiences with developing speech recognition for Medical Transcription -- a system that automatically transcribes notes from doctor-patient conversations. To train these models we used a corpus of anonymized conversations representing approximately 14, hours of speech. Because of noisy transcripts and alignments in the corpus, a significant amount of effort was invested in data cleaning issues. We describe a two-stage strategy we followed for segmenting the data. The data cleanup and development of a matched language model was essential to the success of the CTC based models.
International Journal of Computer Applications 1 , October The communication among human and computer is referred as human computer interface. Speech can be used to commune with computer. The speech recognition research is becoming more and more determined. Today, researchers are trying to making an effort to extend the capabilities of what computers can do with the spoken words.
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