Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information

For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays an important role in producing natural and intelligible speech. Although inter-utterance linguistic information can influence the speech interpretation of the target utterance, previous works on PSP mainly focus on utilizing intrautterance linguistic information of the current utterance only. This work proposes to use inter-utterance linguistic information to improve the performance of PSP. Multi-level contextual information, which includes both inter-utterance and intrautterance linguistic information, is extracted by a hierarchical encoder from character level, utterance level and discourse level of the input text. Then a multi-task learning (MTL) decoder predicts prosodic boundaries from multi-level contextual information. Objective evaluation results on two datasets show that our method achieves better F1 scores in predicting prosodic word (PW), prosodic phrase (PPH) and intonational phrase (IPH). It demonstrates the effectiveness of using multi-level contextual information for PSP. Subjective preference tests also indicate the naturalness of synthesized speeches are improved.Comment: Accepted by Interspeech202

TurnGPT: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog

Syntactic and pragmatic completeness is known to be important for turn-taking prediction, but so far machine learning models of turn-taking have used such linguistic information in a limited way. In this paper, we introduce TurnGPT, a transformer-based language model for predicting turn-shifts in spoken dialog. The model has been trained and evaluated on a variety of written and spoken dialog datasets. We show that the model outperforms two baselines used in prior work. We also report on an ablation study, as well as attention and gradient analyses, which show that the model is able to utilize the dialog context and pragmatic completeness for turn-taking prediction. Finally, we explore the model's potential in not only detecting, but also projecting, turn-completions.Comment: Accepted to Findings of ACL: EMNLP 202

Towards text-based prediction of phrasal prominence

The objective of this thesis was text-based prediction of phrasal prominence. Improving natural sounding speech synthesis motivated the task, because phrasal prominence, which depicts the relative saliency of words within a phrase, is a natural part of spoken language. Following the majority of previous research, prominence is predicted on binary level derived from a symbolic representation of pitch movements. In practice, new classifiers and new models from different fields of natural language processing were explored. Applicability of spatial and graph-based language models was tested by proposing such features as word vectors, a high-dimensional vector-space representation, and DegExt, a keyword weighting method. Support vector machines (SVMs) were used due to their widespread suitability to supervised classification tasks with high-dimensional continuous-valued input. Linear inner product and non-linear radial basis function (RBF) were used as kernels. Furthermore, hidden Markov support vector machines (HM-SVMs) were evaluated to investigate benefits of sequential classification. The experiments on the widely used Boston University Radio News Corpus (BURNC) were successful in two major ways: Firstly, the non-linear support vector machine along with the best performing features achieved similar performance than the previous state-of-the-art approach reported by Rangarajan et al. [RNB06]. Secondly, newly proposed features based on word vectors moderately outperformed part-of-speech tags, which has been inevitably the best performing feature throughout the research of text-based prominence prediction

Synthesising prosody with insufficient context

Prosody is a key component in human spoken communication, signalling emotion, attitude, information structure, intention, and other communicative functions through perceived variation in intonation, loudness, timing, and voice quality. However, the prosody in text-to-speech (TTS) systems is often monotonous and adds no additional meaning to the text. Synthesising prosody is difficult for several reasons: I focus on three challenges. First, prosody is embedded in the speech signal, making it hard to model with machine learning. Second, there is no clear orthography for prosody, meaning it is underspecified in the input text and making it difficult to directly control. Third, and most importantly, prosody is determined by the context of a speech act, which TTS systems do not, and will never, have complete access to. Without the context, we cannot say if prosody is appropriate or inappropriate. Context is wide ranging, but state-of-the-art TTS acoustic models only have access to phonetic information and limited structural information. Unfortunately, most context is either difficult, expensive, or impos- sible to collect. Thus, fully specified prosodic context will never exist. Given there is insufficient context, prosody synthesis is a one-to-many generative task: it necessitates the ability to produce multiple renditions. To provide this ability, I propose methods for prosody control in TTS, using either explicit prosody features, such as F0 and duration, or learnt prosody representations disentangled from the acoustics. I demonstrate that without control of the prosodic variability in speech, TTS will produce average prosody—i.e. flat and monotonous prosody. This thesis explores different options for operating these control mechanisms. Random sampling of a learnt distribution of prosody produces more varied and realistic prosody. Alternatively, a human-in-the-loop can operate the control mechanism—using their intuition to choose appropriate prosody. To improve the effectiveness of human-driven control, I design two novel approaches to make control mechanisms more human interpretable. Finally, it is important to take advantage of additional context as it becomes available. I present a novel framework that can incorporate arbitrary additional context, and demonstrate my state-of- the-art context-aware model of prosody using a pre-trained and fine-tuned language model. This thesis demonstrates empirically that appropriate prosody can be synthesised with insufficient context by accounting for unexplained prosodic variation