Hierarchical representation and estimation of prosody using continuous wavelet transform. (September 2017)

• Record Type: Journal Article
• Authors: Suni, Antti; Šimko, Juraj; Aalto, Daniel; Vainio, Martti
• Is Part Of: Computer speech & language. Volume 45(2017)
• Page Start: 123
• Page End: 136
• Publication Date: 2017-09
• Subjects: Phonetics -- Prosody -- Speech synthesis -- Wavelets; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2016.11.001
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: We introduce a wavelet based representation system for speech prosody. Emergent hierarchy from f 0, intensity and duration. Prominences and boundaries are represented in one framework. System allows for efficient analysis and annotation of prosodic events. The unsupervised prosodic labelling scheme is comparable with supervised methods. Abstract: Prominences and boundaries are the essential constituents of prosodic structure in speech. They provide for means to chunk the speech stream into linguistically relevant units by providing them with relative saliences and demarcating them within utterance structures. Prominences and boundaries have both been widely used in both basic research on prosody as well as in text-to-speech synthesis. However, there are no representation schemes that would provide for both estimating and modelling them in a unified fashion. Here we present an unsupervised unified account for estimating and representing prosodic prominences and boundaries using a scale-space analysis based on continuous wavelet transform. The methods are evaluated and compared to earlier work using the Boston University Radio News corpus. The results show that the proposed method is comparable with the best published supervised annotation methods.