Affective speaker state analysis in the presence of reverberation

Abstract

Little is known about the impact of reverberation on performance of running speaker state classification systems. This study thus aims to approach the topic by measuring effects on a state-of-the-art engine with consideration of six public room impulse responses for convolution of the affective speech signals of three standard datasets comprising of emotion and interest. Speech data thereby is given by this year’s INTERSPEECH Paralinguistic Challenge corpus TUM AVIC and the frequently used Berlin and eNTERFACE sets. The room impulse responses comprise rooms in private apartments, chapels, a factory hall, and a van. Speaker independent performance after speaker adaptation is investigated. To cope with reverberation, matched condition learning and acoustic space adaptation are considered as efficient means. By that a report is provided on suitability of feature types given the type of impulse response. In the result almost all occurring corruption arising from reverberation can be restored, yet the general impact varies with the type of room or acoustic environment.