Projects:2017s2-205 Multi-Profile Parallel Speech-to Text Transcriber

Summary

The aim of this project is to produce a speech transcriber prototype using Dragon Naturally Speaking (DNS) that can transcribe live recording through a single microphone and recognize multiple voices. The proposed prototype recognizes the speakers by comparing the confidence scores generated by DNS for each utterance. The confidence score is used as a measure of transcription accuracy. The main deliverables of this project are to successfully perform transcription for multiple speakers and evaluate the transcription accuracy. Users are required to create and train their profiles by dictating and making corrections to enable DNS to analyze acoustic data such as accent, speech pattern and other variables. The results from several experiments have proven that sufficient profile training is a necessity to achieve high transcription accuracy. The future progress of this project would be to continue conducting more experiments that consider different types of acoustic variability to validate the reliability of the prototype.