Roger K. Moore

Hidden Markov model decomposition of speech and noise

The problem of automatic speech recognition in the presence of interfering signals and noise with statistical characteristics ranging from stationary to fast changing and impulsive is discussed. A technique of signal decomposition using hidden Markov models is described. This is a generalization of conventional hidden Markov modeling that provides an optimal method of decomposing simultaneous processes. The technique exploits the ability of hidden Markov models to model dynamically varying signals in order to accommodate concurrent processes, including interfering signals as complex as speech. This form of signal decomposition has wide implications for signal separation in general and improved speech modeling in particular. The application of decomposition to the problem of recognition of speech contaminated with noise is emphasized.<<ETX>>

Using linguistic cues for the automatic recognition of personality in conversation and text

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speakers personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using self-reports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.

Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition

Semi-Markov models have been proposed as a mechanism for overcoming some of the limitations inherent in first-order Markov modelling of speech signals. Results have been presented which show that these models provide an appropriate framework for modelling durational structure and can lead to significant improvements in recognition accuracy.

A Bayesian explanation of the ‘Uncanny Valley’ effect and related psychological phenomena

There are a number of psychological phenomena in which dramatic emotional responses are evoked by seemingly innocuous perceptual stimuli. A well known example is the ‘uncanny valley’ effect whereby a near human-looking artifact can trigger feelings of eeriness and repulsion. Although such phenomena are reasonably well documented, there is no quantitative explanation for the findings and no mathematical model that is capable of predicting such behavior. Here I show (using a Bayesian model of categorical perception) that differential perceptual distortion arising from stimuli containing conflicting cues can give rise to a perceptual tension at category boundaries that could account for these phenomena. The model is not only the first quantitative explanation of the uncanny valley effect, but it may also provide a mathematical explanation for a range of social situations in which conflicting cues give rise to negative, fearful or even violent reactions.

Handbook of Multimodal and Spoken Dialogue Systems

Human language is said to come in two forms: there are spoken languages that languages need to be considered as essentially heterogeneous systems that An International Handbook. A multimodal and multifocal dialogue corpus. dialogue systems and offers ideas on how to continue along the path GIBBON D., MERTINS I., MOORE R. (eds), Handbook of Multimodal and Spoken. A Review and Meta-Analysis of Multimodal Affect Detection Systems human-computer interaction handbook: fundamentals, evolving technologies and emerging Predicting emotion in spoken dialogue from multiple knowledge sources.

Handbook of Multimodal and Spoken Dialogue Systems: Resources, Terminology and Product Evaluation

Editorial Preface. 1. Representation and annotation of dialogue. 2. Audio-visual and multimodel speech-based systems. 3. Consumer off-the-shelf (COTS) product and service evaluation. 4. Terminology for spoken language systems. 5. Reference materials. Bibliographical references. List of abbreviations. Index. CD-ROM disclaimer.

Spoken language processing: Piecing together the puzzle

Attempting to understand the fundamental mechanisms underlying spoken language processing, whether it is viewed as behaviour exhibited by human beings or as a faculty simulated by machines, is one of the greatest scientific challenges of our age. Despite tremendous achievements over the past 50 or so years, there is still a long way to go before we reach a comprehensive explanation of human spoken language behaviour and can create a technology with performance approaching or exceeding that of a human being. It is argued that progress is hampered by the fragmentation of the field across many different disciplines, coupled with a failure to create an integrated view of the fundamental mechanisms that underpin one organisms ability to communicate with another. This paper weaves together accounts from a wide variety of different disciplines concerned with the behaviour of living systems - many of them outside the normal realms of spoken language - and compiles them into a new model: PRESENCE (PREdictive SENsorimotor Control and Emulation). It is hoped that the results of this research will provide a sufficient glimpse into the future to give breath to a new generation of research into spoken language processing by mind or machine.

A Dynamic Programming Algorithm for the Distance Between Two Finite Areas

The problems of speech recognition and orthographic word correction have been greatly mitigated by the use of dynamic programming techniques for finding the distance between two finite sequences. This paper extends the technique into two dimensions, and presents an algorithm for finding the distance between two finite areas. Applications of the algorithm are suggested.

Modelling asynchrony in speech using elementary single-signal decomposition

Although the possibility of asynchrony between different components of the speech spectrum has been acknowledged, its potential effect on automatic speech recogniser performance has only recently been studied. This paper presents the results of continuous speech recognition experiments in which such asynchrony is accommodated using a variant of HMM decomposition. The paper begins with an investigation of the effects of partitioning the speech spectrum explicitly into subbands. Asynchrony between these sub-bands is then accommodated, resulting in a significant decrease in word errors. The same decomposition technique has previously been used successfully to compensate for asynchrony between the two input streams in an audiovisual speech recognition system.

Noise compensation algorithms for use with hidden Markov model based speech recognition

A preliminary theoretical and experimental examination is made of three noise compensation techniques. The three techniques are those due to: Klatt (1976); Bridle et al (1984); and Holmes & Sedgwick (1986). The first two of these techniques have been re-interpreted for use within a hidden Markov model based recogniser. A description is given of how this was done, together with a discussion on some implementation considerations. Experimental results are given for the performance of the algorithms at various signal-to-noise ratios. The principles of recognition in noise are discussed from an implementation point of view and it is shown how the three techniques can be viewed as variations on a single theme.<<ETX>>