Speaker Diarization

Machine-transcription of speech into text is very helpful in many scenarios. Consider the case of machine-transcription of conversation between a Doctor and a patient. If we are able partition and identify the segments of patient’s speech from those of doctor’s, then the transcribed text is more structured and can be more helpful for further use. The process of partitioni a given input audio stream into homogeneous segments according to speaker identity is called Speaker Diarization. In this project, we want to implement and improve the state-of-art method for speaker-diarization.

Faculty Supervisor:

Yoshua Bengio

Student:

Vicki Anand

Partner:

Lyrebird AI

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Montréal

Program:

Accelerate

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