Speaker variability in emotion recognition - an adaptation based approach

Abstract

None of the features commonly utilised in automatic emotion classification systems completely disassociate emotion-specific information from speaker-specific information. Consequently, this speaker-specific variability adversely affects the performance of the emotion classification system and in existing systems is frequently mitigated by some form of speaker normalisation. Speaker adaptation offers an alternative to normalisation and this paper proposes a novel bootstrapping technique which involves selecting appropriate initial models from a large training pool, prior to speaker adaptation of emotion models in the context of GMM based emotion classification as an alternative to speaker normalisation. Evaluations on the LDC Emotional Prosody and the FAU Aibo corpora reveal that an emotion classification system based on the proposed bootstrapping method outperforms systems based on speaker normalisation as long as a small amount of labelled adaptation data is available. It also outperforms speaker adaption from common initial models estimated from all training speakers.