Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech

Automatic Visual Speech Recognition

Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Visual speech synthesis using dynamic visemes, contextual features and DNNs

This paper examines methods to improve visual speech synthesis from a text input using a deep neural network (DNN). Two representations of the input text are considered, namely into phoneme sequences or dynamic viseme sequences. From these sequences, contextual features are extracted that include information at varying linguistic levels, from frame level down to the utterance level. These are extracted from a broad sliding window that captures context and produces features that are input into the DNN to estimate visual features. Experiments first compare the accuracy of these visual features against an HMM baseline method which establishes that both the phoneme and dynamic viseme systems perform better with best performance obtained by a combined phoneme-dynamic viseme system. An investigation into the features then reveals the importance of the frame level information which is able to avoid discontinuities in the visual feature sequence and produces a smooth and realistic output

Confusion modelling for lip-reading

Lip-reading is mostly used as a means of communication by people with hearing di�fficulties. Recent work has explored the automation of this process, with the aim of building a speech recognition system entirely driven by lip movements. However, this work has so far produced poor results because of factors such as high variability of speaker features, diffi�culties in mapping from visual features to speech sounds, and high co-articulation of visual features. The motivation for the work in this thesis is inspired by previous work in dysarthric speech recognition [Morales, 2009]. Dysathric speakers have poor control over their articulators, often leading to a reduced phonemic repertoire. The premise of this thesis is that recognition of the visual speech signal is a similar problem to recog- nition of dysarthric speech, in that some information about the speech signal has been lost in both cases, and this brings about a systematic pattern of errors in the decoded output. This work attempts to exploit the systematic nature of these errors by modelling them in the framework of a weighted finite-state transducer cascade. Results indicate that the technique can achieve slightly lower error rates than the conventional approach. In addition, it explores some interesting more general questions for automated lip-reading

Expressive Modulation of Neutral Visual Speech

The need for animated graphical models of the human face is commonplace in the movies, video games and television industries, appearing in everything from low budget advertisements and free mobile apps, to Hollywood blockbusters costing hundreds of millions of dollars. Generative statistical models of animation attempt to address some of the drawbacks of industry standard practices such as labour intensity and creative inflexibility. This work describes one such method for transforming speech animation curves between different expressive styles. Beginning with the assumption that expressive speech animation is a mix of two components, a high-frequency speech component (the content) and a much lower-frequency expressive component (the style), we use Independent Component Analysis (ICA) to identify and manipulate these components independently of one another. Next we learn how the energy for different speaking styles is distributed in terms of the low-dimensional independent components model. Transforming the speaking style involves projecting new animation curves into the lowdimensional ICA space, redistributing the energy in the independent components, and finally reconstructing the animation curves by inverting the projection. We show that a single ICA model can be used for separating multiple expressive styles into their component parts. Subjective evaluations show that viewers can reliably identify the expressive style generated using our approach, and that they have difficulty in identifying transformed animated expressive speech from the equivalent ground-truth

Decoding visemes: improving machine lip-reading

Abstract This thesis is about improving machine lip-reading, that is, the classi�cation of speech from only visual cues of a speaker. Machine lip-reading is a niche research problem in both areas of speech processing and computer vision. Current challenges for machine lip-reading fall into two groups: the content of the video, such as the rate at which a person is speaking or; the parameters of the video recording for example, the video resolution. We begin our work with a literature review to understand the restrictions current technology limits machine lip-reading recognition and conduct an experiment into resolution a�ects. We show that high de�nition video is not needed to successfully lip-read with a computer. The term \viseme" is used in machine lip-reading to represent a visual cue or gesture which corresponds to a subgroup of phonemes where the phonemes are indistinguishable in the visual speech signal. Whilst a viseme is yet to be formally de�ned, we use the common working de�nition `a viseme is a group of phonemes with identical appearance on the lips'. A phoneme is the smallest acoustic unit a human can utter. Because there are more phonemes per viseme, mapping between the units creates a many-to-one relationship. Many mappings have been presented, and we conduct an experiment to determine which mapping produces the most accurate classi�cation. Our results show Lee's [82] is best. Lee's classi�cation also outperforms machine lip-reading systems which use the popular Fisher [48] phonemeto- viseme map. Further to this, we propose three methods of deriving speaker-dependent phonemeto- viseme maps and compare our new approaches to Lee's. Our results show the ii iii sensitivity of phoneme clustering and we use our new knowledge for our �rst suggested augmentation to the conventional lip-reading system. Speaker independence in machine lip-reading classi�cation is another unsolved obstacle. It has been observed, in the visual domain, that classi�ers need training on the test subject to achieve the best classi�cation. Thus machine lip-reading is highly dependent upon the speaker. Speaker independence is the opposite of this, or in other words, is the classi�cation of a speaker not present in the classi�er's training data. We investigate the dependence of phoneme-to-viseme maps between speakers. Our results show there is not a high variability of visual cues, but there is high variability in trajectory between visual cues of an individual speaker with the same ground truth. This implies a dependency upon the number of visemes within each set for each individual. Finally, we investigate how many visemes is the optimum number within a set. We show the phoneme-to-viseme maps in literature rarely have enough visemes and the optimal number, which varies by speaker, ranges from 11 to 35. The last di�culty we address is decoding from visemes back to phonemes and into words. Traditionally this is completed using a language model. The language model unit is either: the same as the classi�er, e.g. visemes or phonemes; or the language model unit is words. In a novel approach we use these optimum range viseme sets within hierarchical training of phoneme labelled classi�ers. This new method of classi�er training demonstrates signi�cant increase in classi�cation with a word language network