Recognition of interest in human conversational speech
Björn Wolfgang Schuller, Niels Köhler, Ronald Müller, Gerhard Rigoll · 2006
Recognition of interest of a speaker within a human dialog bears great potential in many commercial applications.Within this work we therefore introduce an approach that analyses acoustic and linguistic cues of a spoken utterance.A systematic generation of more than 5k hi-level features basing on prosodic and spectral feature contours by means of descriptive statistical analysis and subsequent feature space optimization is used to find relevant acoustic attributes.For linguistic information integration a bag-of-words representation is used relying on a speech recognizer's output.One main aspect is the database of more than 2k spontaneous sub-speaker turns recorded and annotated for this analysis.Several influence factors as microphone distance and ASR versus annotation of spoken content are discussed.Overall remarkable performance of a running prototype can be reported discriminating between three levels of interest.