Animation of a hierarchical image based facial model and perceptual analysis of visual speech

In this Thesis a hierarchical image-based 2D talking head model is presented, together with robust automatic and semi-automatic animation techniques, and a novel perceptual method for evaluating visual-speech based on the McGurk effect. The novelty of the hierarchical facial model stems from the fact that sub-facial areas are modelled individually. To produce a facial animation, animations for a set of chosen facial areas are first produced, either by key-framing sub-facial parameter values, or using a continuous input speech signal, and then combined into a full facial output. Modelling hierarchically has several attractive qualities. It isolates variation in sub-facial regions from the rest of the face, and therefore provides a high degree of control over different facial parts along with meaningful image based animation parameters. The automatic synthesis of animations may be achieved using speech not originally included in the training set. The model is also able to automatically animate pauses, hesitations and non-verbal (or non-speech related) sounds and actions. To automatically produce visual-speech, two novel analysis and synthesis methods are proposed. The first method utilises a Speech-Appearance Model (SAM), and the second uses a Hidden Markov Coarticulation Model (HMCM) - based on a Hidden Markov Model (HMM). To evaluate synthesised animations (irrespective of whether they are rendered semi automatically, or using speech), a new perceptual analysis approach based on the McGurk effect is proposed. This measure provides both an unbiased and quantitative method for evaluating talking head visual speech quality and overall perceptual realism. A combination of this new approach, along with other objective and perceptual evaluation techniques, are employed for a thorough evaluation of hierarchical model animations.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Animation of a hierarchical image based facial model and perceptual analysis of visual speech

In this Thesis a hierarchical image-based 2D talking head model is presented, together with robust automatic and semi-automatic animation techniques, and a novel perceptual method for evaluating visual-speech based on the McGurk effect. The novelty of the hierarchical facial model stems from the fact that sub-facial areas are modelled individually. To produce a facial animation, animations for a set of chosen facial areas are first produced, either by key-framing sub-facial parameter values, or using a continuous input speech signal, and then combined into a full facial output. Modelling hierarchically has several attractive qualities. It isolates variation in sub-facial regions from the rest of the face, and therefore provides a high degree of control over different facial parts along with meaningful image based animation parameters. The automatic synthesis of animations may be achieved using speech not originally included in the training set. The model is also able to automatically animate pauses, hesitations and non-verbal (or non-speech related) sounds and actions. To automatically produce visual-speech, two novel analysis and synthesis methods are proposed. The first method utilises a Speech-Appearance Model (SAM), and the second uses a Hidden Markov Coarticulation Model (HMCM) - based on a Hidden Markov Model (HMM). To evaluate synthesised animations (irrespective of whether they are rendered semi automatically, or using speech), a new perceptual analysis approach based on the McGurk effect is proposed. This measure provides both an unbiased and quantitative method for evaluating talking head visual speech quality and overall perceptual realism. A combination of this new approach, along with other objective and perceptual evaluation techniques, are employed for a thorough evaluation of hierarchical model animations

Inference in Hidden Markov Models with Explicit State Duration Distributions

In this letter we borrow from the inference techniques developed for unbounded state-cardinality (nonparametric) variants of the HMM and use them to develop a tuning-parameter free, black-box inference procedure for Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have latent states consisting of both discrete state-indicator and discrete state-duration random variables. In contrast to the implicit geometric state duration distribution possessed by the standard HMM, EDHMMs allow the direct parameterisation and estimation of per-state duration distributions. As most duration distributions are defined over the positive integers, truncation or other approximations are usually required to perform EDHMM inference

Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models

Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to firstly estimate the transcription of the adaptation data. By defining a mapping between HMM-based synthesis models and ASR-style models, this paper introduces an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for supplementary acoustic models. Further, this enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201