Redefining Concatenative Speech Synthesis for Use in Spontaneous Conversational Dialogues; A Study with the GBO Corpus

This chapter describes how a very large corpus of conversational speech is being tested as a source of units for concatenative speech synthesis. It shows that the challenge no longer lies in phone-sized unit selection, but in categorising larger units for their affective and pragmatic effect. The work is by nature exploratory, but much progress has been achieved and we now have the beginnings of an understanding of the types of grammar and the ontology of vocal productions that will be required for the interactive synthesis of conversational speech. The chapter describes the processes involved and explains some of the features selected for optimal expressive speech rendering

Automatic vocal recognition of a child's perceived emotional state within the Speechome corpus

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 137-149).With over 230,000 hours of audio/video recordings of a child growing up in the home setting from birth to the age of three, the Human Speechome Project has pioneered a comprehensive, ecologically valid observational dataset that introduces far-reaching new possibilities for the study of child development. By offering In vivo observation of a child's daily life experience at ultra-dense, longitudinal time scales, the Speechome corpus holds great potential for discovering developmental insights that have thus far eluded observation. The work of this thesis aspires to enable the use of the Speechome corpus for empirical study of emotional factors in early child development. To fully harness the benefits of Speechome for this purpose, an automated mechanism must be created to perceive the child's emotional state within this medium. Due to the latent nature of emotion, we sought objective, directly measurable correlates of the child's perceived emotional state within the Speechome corpus, focusing exclusively on acoustic features of the child's vocalizations and surrounding caretaker speech. Using Partial Least Squares regression, we applied these features to build a model that simulates human perceptual heuristics for determining a child's emotional state. We evaluated the perceptual accuracy of models built across child-only, adult-only, and combined feature sets within the overall sampled dataset, as well as controlling for social situations, vocalization behaviors (e.g. crying, laughing, babble), individual caretakers, and developmental age between 9 and 24 months. Child and combined models consistently demonstrated high perceptual accuracy, with overall adjusted R-squared values of 0.54 and 0.58, respectively, and an average of 0.59 and 0.67 per month. Comparative analysis across longitudinal and socio-behavioral contexts yielded several notable developmental and dyadic insights. In the process, we have developed a data mining and analysis methodology for modeling perceived child emotion and quantifying caretaker intersubjectivity that we hope to extend to future datasets across multiple children, as new deployments of the Speechome recording technology are established. Such large-scale comparative studies promise an unprecedented view into the nature of emotional processes in early childhood and potentially enlightening discoveries about autism and other developmental disorders.by Sophia Yuditskaya.S.M

Challenges in analysis and processing of spontaneous speech

Selected and peer-reviewed papers of the workshop entitled Challenges in Analysis and Processing of Spontaneous Speech (Budapest, 2017