Fine-tuning HMMS for nonverbal vocalizations in spontaneous speech: A multicorpus perspective

Phenomena like filled pauses, laughter, breathing, hesitation, etc. play significant role in everyday human-to-human conversation and have a significant influence on speech recognition accuracy [1]. Because of their nature (e. g. long duration), they should be modeled with different number of emitting states and Gaussian mixtures. In this paper we address this question and try to determine the most suitable method for finding these parameters: we provide an examination of two methods for optimization of hidden Markov model (HMM) configurations for better classification and recognition of nonverbal vocalizations within speech. Experiments were conducted on three conversational databases: TUM AVIC, Verbmobil, and SmartKom. These experiments show that with HMMs configurations tailored to a particular database we can achieve 1-3% improvement in speech recognition accuracy with comparison to a baseline topology. An in-depth analysis of discussed methods is provided.