Fast Speech in Unit Selection Speech Synthesis

Moers-Prinz D. Fast Speech in Unit Selection Speech Synthesis. Bielefeld: Universität Bielefeld; 2020.Speech synthesis is part of the everyday life of many people with severe visual disabilities. For those who are reliant on assistive speech technology the possibility to choose a fast speaking rate is reported to be essential. But also expressive speech synthesis and other spoken language interfaces may require an integration of fast speech. Architectures like formant or diphone synthesis are able to produce synthetic speech at fast speech rates, but the generated speech does not sound very natural. Unit selection synthesis systems, however, are capable of delivering more natural output. Nevertheless, fast speech has not been adequately implemented into such systems to date. Thus, the goal of the work presented here was to determine an optimal strategy for modeling fast speech in unit selection speech synthesis to provide potential users with a more natural sounding alternative for fast speech output

Learning disentangled speech representations

A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

Negative vaccine voices in Swedish social media

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination

Identifying prosodic prominence patterns for English text-to-speech synthesis

This thesis proposes to improve and enrich the expressiveness of English Text-to-Speech (TTS) synthesis by identifying and generating natural patterns of prosodic prominence. In most state-of-the-art TTS systems the prediction from text of prosodic prominence relations between words in an utterance relies on features that very loosely account for the combined effects of syntax, semantics, word informativeness and salience, on prosodic prominence. To improve prosodic prominence prediction we first follow up the classic approach in which prosodic prominence patterns are flattened into binary sequences of pitch accented and pitch unaccented words. We propose and motivate statistic and syntactic dependency based features that are complementary to the most predictive features proposed in previous works on automatic pitch accent prediction and show their utility on both read and spontaneous speech. Different accentuation patterns can be associated to the same sentence. Such variability rises the question on how evaluating pitch accent predictors when more patterns are allowed. We carry out a study on prosodic symbols variability on a speech corpus where different speakers read the same text and propose an information-theoretic definition of optionality of symbolic prosodic events that leads to a novel evaluation metric in which prosodic variability is incorporated as a factor affecting prediction accuracy. We additionally propose a method to take advantage of the optionality of prosodic events in unit-selection speech synthesis. To better account for the tight links between the prosodic prominence of a word and the discourse/sentence context, part of this thesis goes beyond the accent/no-accent dichotomy and is devoted to a novel task, the automatic detection of contrast, where contrast is meant as a (Information Structure’s) relation that ties two words that explicitly contrast with each other. This task is mainly motivated by the fact that contrastive words tend to be prosodically marked with particularly prominent pitch accents. The identification of contrastive word pairs is achieved by combining lexical information, syntactic information (which mainly aims to identify the syntactic parallelism that often activates contrast) and semantic information (mainly drawn from the Word- Net semantic lexicon), within a Support Vector Machines classifier. Once we have identified patterns of prosodic prominence we propose methods to incorporate such information in TTS synthesis and test its impact on synthetic speech naturalness trough some large scale perceptual experiments. The results of these experiments cast some doubts on the utility of a simple accent/no-accent distinction in Hidden Markov Model based speech synthesis while highlight the importance of contrastive accents