Abstract

Speaker Role Recognition (SRR) is usually addressed either as an independent classification task, or as a subsequent step after a speaker clustering module. However, the first approach does not take speaker-specific variabilities into account, while the second one results in error propagation. In this work we propose the integration of an audio-based speaker clustering algorithm with a language-aided role recognizer into a meta-classifier which takes both modalities into account. That way, we can treat separately any speaker-specific and role-specific characteristics before combining the relevant information together. The method is evaluated on two corpora of different conditions with interactions between a clinician and a patient and it is shown that it yields superior results for the SRR task.

Date

2018

Authors

Nikolaos Flemotomos, Pavlos Papadopoulos, James Gibson, Shrikanth S Narayanan

Conference

Interspeech

Pages

1378-1382