Statistical parametric speech synthesis using conversational data and phenomena

Statistical parametric text-to-speech synthesis currently relies on predefined and highly controlled prompts read in a “neutral” voice. This thesis presents work on utilising recordings of free conversation for the purpose of filled pause synthesis and as an inspiration for improved general modelling of speech for text-to-speech synthesis purposes. A corpus of both standard prompts and free conversation is presented and the potential usefulness of conversational speech as the basis for text-to-speech voices is validated. Additionally, through psycholinguistic experimentation it is shown that filled pauses can have potential subconscious benefits to the listener but that current text-to-speech voices cannot replicate these effects. A method for pronunciation variant forced alignment is presented in order to obtain a more accurate automatic speech segmentation something which is particularly bad for spontaneously produced speech. This pronunciation variant alignment is utilised not only to create a more accurate underlying acoustic model, but also as the driving force behind creating more natural pronunciation prediction at synthesis time. While this improves both the standard and spontaneous voices the naturalness of spontaneous speech based voices still lags behind the quality of voices based on standard read prompts. Thus, the synthesis of filled pauses is investigated in relation to specific phonetic modelling of filled pauses and through techniques for the mixing of standard prompts with spontaneous utterances in order to retain the higher quality of standard speech based voices while still utilising the spontaneous speech for filled pause modelling. A method for predicting where to insert filled pauses in the speech stream is also developed and presented, relying on an analysis of human filled pause usage and a mix of language modelling methods. The method achieves an insertion accuracy in close agreement with human usage. The various approaches are evaluated and their improvements documented throughout the thesis, however, at the end the resulting filled pause quality is assessed through a repetition of the psycholinguistic experiments and an evaluation of the compilation of all developed methods

An investigation into interactional patterns for Alzheimer's Disease recognition in Natural dialogues

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by memory loss, together with cognitive deficits affecting language, emotional affect, and interactional communication. Diagnosis and assessment of AD is formally based on the judgment of clinicians, commonly using semi-structured interviews in a clinical setting. Manual diagnosis is therefore slow, resource-heavy, and hard to access, so many people don't get diagnosed - and therefore using some kind of automatic method would help. Using the most recent advances in deep learning, machine learning, and natural language processing, this thesis empirically explores how content-free, interaction patterns are helpful in developing models capable of identifying AD from natural conversations with a focus on particular phenomena found useful in conversational analysis studies. The models presented in this thesis use lexical, disfluency, interactional, acoustic, and pause information to learn the symptoms of Alzheimer's disease from text and audio modalities. This thesis comprises two parts. In the first part, by studying a conversational corpus, we find there are certain phenomena that are really strongly indicative of differences between AD and Non-AD. This analysis shows that interaction patterns are different between an AD patient and a Non-AD patient, including types of questions asked from patients, their responses, delay in responses in the form of pauses, clarification questions, signaling non-understanding, and repetition of questions. Although it is a challenging problem due to the fact that these dialogue acts are so rare, we show that it is possible to develop models that can automatically detect these classes. The second part then shifts to look at AD diagnosis itself by looking into interactional features including pause information, disfluencies within patients speech, communication breakdowns at speaker changes in certain situations, Ngram dialogue act sequences. We found out that there are longer pauses within the AD patients utterances and more attributable silences in response to questions as compared to Non-AD patients. It also showed that using different fusion techniques with speech and text modality has maximise the combination and use of different feature sets showing that these features/techniques can give quite good accurate and effective AD diagnosis. These interaction patterns may serve as an index of internal cognitive processes that help in differentiating AD patients and Non-AD patients and may be used as an integral part of language assessment in clinical settings

Speech technologies for the audiovisual and multimedia interaction environments

361 p

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli