Generation of prosody and speech for Mandarin Chinese

Ph.DDOCTOR OF PHILOSOPH

Pronunciation modelling in end-to-end text-to-speech synthesis

Sequence-to-sequence (S2S) models in text-to-speech synthesis (TTS) can achieve high-quality naturalness scores without extensive processing of text-input. Since S2S models have been proposed in multiple aspects of the TTS pipeline, the field has focused on embedding the pipeline toward End-to-End (E2E-) TTS where a waveform is predicted directly from a sequence of text or phone characters. Early work on E2ETTS in English, such as Char2Wav [1] and Tacotron [2], suggested that phonetisation (lexicon-lookup and/or G2P modelling) could be implicitly learnt in a text-encoder during training. The benefits of a learned text encoding include improved modelling of phonetic context, which make contextual linguistic features traditionally used in TTS pipelines redundant [3]. Subsequent work on E2E-TTS has since shown similar naturalness scores with text- or phone-input (e.g. as in [4]). Successful modelling of phonetic context has led some to question the benefit of using phone- instead of text-input altogether (see [5]). The use of text-input brings into question the value of the pronunciation lexicon in E2E-TTS. Without phone-input, a S2S encoder learns an implicit grapheme-tophoneme (G2P) model from text-audio pairs during training. With common datasets for E2E-TTS in English, I simulated implicit G2P models, finding increased error rates compared to a traditional, lexicon-based G2P model. Ultimately, successful G2P generalisation is difficult for some words (e.g. foreign words and proper names) since the knowledge to disambiguate their pronunciations may not be provided by the local grapheme context and may require knowledge beyond that contained in sentence-level text-audio sequences. When test stimuli were selected according to G2P difficulty, increased mispronunciations in E2E-TTS with text-input were observed. Following the proposed benefits of subword decomposition in S2S modelling in other language tasks (e.g. neural machine translation), the effects of morphological decomposition were investigated on pronunciation modelling. Learning of the French post-lexical phenomenon liaison was also evaluated. With the goal of an inexpensive, large-scale evaluation of pronunciation modelling, the reliability of automatic speech recognition (ASR) to measure TTS intelligibility was investigated. A re-evaluation of 6 years of results from the Blizzard Challenge was conducted. ASR reliably found similar significant differences between systems as paid listeners in controlled conditions in English. An analysis of transcriptions for words exhibiting difficult-to-predict G2P relations was also conducted. The E2E-ASR Transformer model used was found to be unreliable in its transcription of difficult G2P relations due to homophonic transcription and incorrect transcription of words with difficult G2P relations. A further evaluation of representation mixing in Tacotron finds pronunciation correction is possible when mixing text- and phone-inputs. The thesis concludes that there is still a place for the pronunciation lexicon in E2E-TTS as a pronunciation guide since it can provide assurances that G2P generalisation cannot

The analysis of breathing and rhythm in speech

Speech rhythm can be described as the temporal patterning by which speech events, such as vocalic onsets, occur. Despite efforts to quantify and model speech rhythm across languages, it remains a scientifically enigmatic aspect of prosody. For instance, one challenge lies in determining how to best quantify and analyse speech rhythm. Techniques range from manual phonetic annotation to the automatic extraction of acoustic features. It is currently unclear how closely these differing approaches correspond to one another. Moreover, the primary means of speech rhythm research has been the analysis of the acoustic signal only. Investigations of speech rhythm may instead benefit from a range of complementary measures, including physiological recordings, such as of respiratory effort. This thesis therefore combines acoustic recording with inductive plethysmography (breath belts) to capture temporal characteristics of speech and speech breathing rhythms. The first part examines the performance of existing phonetic and algorithmic techniques for acoustic prosodic analysis in a new corpus of rhythmically diverse English and Mandarin speech. The second part addresses the need for an automatic speech breathing annotation technique by developing a novel function that is robust to the noisy plethysmography typical of spontaneous, naturalistic speech production. These methods are then applied in the following section to the analysis of English speech and speech breathing in a second, larger corpus. Finally, behavioural experiments were conducted to investigate listeners' perception of speech breathing using a novel gap detection task. The thesis establishes the feasibility, as well as limits, of automatic methods in comparison to manual annotation. In the speech breathing corpus analysis, they help show that speakers maintain a normative, yet contextually adaptive breathing style during speech. The perception experiments in turn demonstrate that listeners are sensitive to the violation of these speech breathing norms, even if unconsciously so. The thesis concludes by underscoring breathing as a necessary, yet often overlooked, component in speech rhythm planning and production

Suprasegmental representations for the modeling of fundamental frequency in statistical parametric speech synthesis

Statistical parametric speech synthesis (SPSS) has seen improvements over recent years, especially in terms of intelligibility. Synthetic speech is often clear and understandable, but it can also be bland and monotonous. Proper generation of natural speech prosody is still a largely unsolved problem. This is relevant especially in the context of expressive audiobook speech synthesis, where speech is expected to be fluid and captivating. In general, prosody can be seen as a layer that is superimposed on the segmental (phone) sequence. Listeners can perceive the same melody or rhythm in different utterances, and the same segmental sequence can be uttered with a different prosodic layer to convey a different message. For this reason, prosody is commonly accepted to be inherently suprasegmental. It is governed by longer units within the utterance (e.g. syllables, words, phrases) and beyond the utterance (e.g. discourse). However, common techniques for the modeling of speech prosody - and speech in general - operate mainly on very short intervals, either at the state or frame level, in both hidden Markov model (HMM) and deep neural network (DNN) based speech synthesis. This thesis presents contributions supporting the claim that stronger representations of suprasegmental variation are essential for the natural generation of fundamental frequency for statistical parametric speech synthesis. We conceptualize the problem by dividing it into three sub-problems: (1) representations of acoustic signals, (2) representations of linguistic contexts, and (3) the mapping of one representation to another. The contributions of this thesis provide novel methods and insights relating to these three sub-problems. In terms of sub-problem 1, we propose a multi-level representation of f0 using the continuous wavelet transform and the discrete cosine transform, as well as a wavelet-based decomposition strategy that is linguistically and perceptually motivated. In terms of sub-problem 2, we investigate additional linguistic features such as text-derived word embeddings and syllable bag-of-phones and we propose a novel method for learning word vector representations based on acoustic counts. Finally, considering sub-problem 3, insights are given regarding hierarchical models such as parallel and cascaded deep neural networks

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

Developing online parallel corpus-based processing tools for translation research and pedagogy

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Comunicação e Expressão, Programa de Pós-Graduação em Letras/Inglês e Literatura Correspondente, Florianópolis, 2013.Abstract : This study describes the key steps in developing online parallel corpus-based tools for processing COPA-TRAD (copa-trad.ufsc.br), a parallel corpus compiled for translation research and pedagogy. The study draws on Fernandes s (2009) proposal for corpus compilation, which divides the compiling process into three main parts: corpus design, corpus building and corpus processing. This compiling process received contributions from the good development practices of Software Engineering, especially the ones advocated by Pressman (2011). The tools developed can, for example, assist in the investigation of certain types of texts and translational practices related to certain linguistic patterns such as collocations and semantic prosody. As a result of these applications, COPA-TRAD becomes a suitable tool for the investigation of empirical phenomena with a view to translation research and pedagogy.Este estudo descreve as principais etapas no desenvolvimento de ferramentas online com base em corpus para o processamento do COPA-TRAD (Corpus Paralelo de Tradução - www.copa-trad.ufsc.br), um corpus paralelo compilado para a pesquisa e ensino de tradução. Para a compilação do corpus, o estudo utiliza a proposta de Fernandes (2009) que divide o processo de compilação em três etapas principais: desenho do corpus, construção do corpus e processamento do corpus. Este processo de compilação recebeu contribuições das boas práticas de desenvolvimento fornecidas pela Engenharia de Software, especialmente as que foram sugeridas por Pressman (2011). As ferramentas desenvolvidas podem, por exemplo, auxiliar na investigação de certos tipos de textos, bem como em práticas tradutórias relacionadas a certos padrões linguísticos tais como colocações e prosódia semântica. Como resultado dessas aplicações, o COPA-TRAD configura-se em uma ferramenta útil para a investigação empírica de fenômenos tradutórios com vistas à pesquisa e ao ensino de tradução

Computational Approaches to the Syntax–Prosody Interface: Using Prosody to Improve Parsing

Prosody has strong ties with syntax, since prosody can be used to resolve some syntactic ambiguities. Syntactic ambiguities have been shown to negatively impact automatic syntactic parsing, hence there is reason to believe that prosodic information can help improve parsing. This dissertation considers a number of approaches that aim to computationally examine the relationship between prosody and syntax of natural languages, while also addressing the role of syntactic phrase length, with the ultimate goal of using prosody to improve parsing. Chapter 2 examines the effect of syntactic phrase length on prosody in double center embedded sentences in French. Data collected in a previous study were reanalyzed using native speaker judgment and automatic methods (forced alignment). Results demonstrate similar prosodic splitting behavior as in English in contradiction to the original study’s findings. Chapter 3 presents a number of studies examining whether syntactic ambiguity can yield different prosodic patterns, allowing humans and/or computers to resolve the ambiguity. In an experimental study, humans disambiguated sentences with prepositional phrase- (PP)-attachment ambiguity with 49% accuracy presented as text, and 63% presented as audio. Machine learning on the same data yielded an accuracy of 63-73%. A corpus study on the Switchboard corpus used both prosodic breaks and phrase lengths to predict the attachment, with an accuracy of 63.5% for PP-attachment sentences, and 71.2% for relative clause attachment. Chapter 4 aims to identify aspects of syntax that relate to prosody and use these in combination with prosodic cues to improve parsing. The aspects identified (dependency configurations) are based on dependency structure, reflecting the relative head location of two consecutive words, and are used as syntactic features in an ensemble system based on Recurrent Neural Networks, to score parse hypotheses and select the most likely parse for a given sentence. Using syntactic features alone, the system achieved an improvement of 1.1% absolute in Unlabelled Attachment Score (UAS) on the test set, above the best parser in the ensemble, while using syntactic features combined with prosodic features (pauses and normalized duration) led to a further improvement of 0.4% absolute. The results achieved demonstrate the relationship between syntax, syntactic phrase length, and prosody, and indicate the ability and future potential of prosody to resolve ambiguity and improve parsing

Speech technologies for the audiovisual and multimedia interaction environments

361 p