title: Intoxicated Speech Detection: A Fusion Framework with Speaker-Normalized Hierarchical Functionals and GMM Supervectors.
abstract: Segmental and suprasegmental speech signal modulations offer information about paralinguistic content such as affect, age and gender, pathology, and speaker state. Speaker state encompasses medium-term, temporary physiological phenomena influenced by internal or external biochemical actions (e.g., sleepiness, alcohol intoxication). Perceptual and computational research indicates that detecting speaker state from speech is a challenging task. In this paper, we present a system constructed with multiple representations of prosodic and spectral features that provided the best result at the Intoxication Subchallenge of Interspeech 2011 on the Alcohol Language Corpus. We discuss the details of each classifier and show that fusion improves performance. We additionally address the question of how best to construct a speaker state detection system in terms of robust and practical marginalization of associated variability such as through modeling speakers, utterance type, gender, and utterance length. As is the case in human perception, speaker normalization provides significant improvements to our system. We show that a held-out set of baseline (sober) data can be used to achieve comparable gains to other speaker normalization techniques. Our fused frame-level statistic-functional systems, fused GMM systems, and final combined system achieve unweighted average recalls (UARs) of 69.7%, 65.1%, and 68.8%, respectively, on the test set. More consistent numbers compared to development set results occur with matched-prompt training, where the UARs are 70.4%, 66.2%, and 71.4%, respectively. The combined system improves over the Challenge baseline by 5.5% absolute (8.4% relative), also improving upon our previously best result.
authors.affiliation: Signal Analysis & Interpretation Laboratory (SAIL), University of Southern California, 3710 McClintock Ave., Los Angeles, CA 90089, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Bone
authors.affiliation: Signal Analysis & Interpretation Laboratory (SAIL), University of Southern California, 3710 McClintock Ave., Los Angeles, CA 90089, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.affiliation: Signal Analysis & Interpretation Laboratory (SAIL), University of Southern California, 3710 McClintock Ave., Los Angeles, CA 90089, USA.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Black
authors.affiliation: Signal Analysis & Interpretation Laboratory (SAIL), University of Southern California, 3710 McClintock Ave., Los Angeles, CA 90089, USA ; Department of Linguistics, University of Southern California (USC), 3620 McClintock Ave., Los Angeles, CA 90089, USA.
authors.forename: Shrikanth S
authors.initials: SS
authors.lastname: Narayanan
keywords: GMM supervectors; cognitive and motor load; hierarchical features; intoxication detection; speaker normalization; speaker state