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Speaker spotting : automatic annotation of audio data with speaker identity

Speaker spotting is an application of automatic speaker recognition to multimedia indexing. It aims to find if and when a predetermined individual or member of a predetermined set of individuals is speaking. This project examines the development of a speaker spotter for broadcast television news. In particular, it examines the issue of open-set speaker identification, or differentiating between the individuals being spotted (target) and everyone else (non-target), by testing two different models for non-target speakers, and through implementation of a cohort threshold. Also, issues in segmentation are investigated through the use of speaker transition probabilities and overlapping decision segments. A spotting accuracy of 70% to 80% for both target and non-target speakers is achieved on a test hour of broadcast news. Thesis Supervisor: Christopher Schmandt Title: Principal Research Scientist