A detection-based pattern recognition framework and its applications

The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation. Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneous information sources are combined together and the high-level context is incorporated as additional information at certain stages. A detection-based framework is a â divide-and-conquerâ design paradigm for pattern recognition problems, which will decompose a conceptually difficult problem into many elementary sub-problems that can be handled directly and reliably. Some information fusion strategies will be employed to integrate the evidence from a lower level to form the evidence at a higher level. Such a fusion procedure continues until reaching the top level. Generally, a detection-based framework has many advantages: (1) more flexibility in both detector design and fusion strategies, as these two parts can be optimized separately; (2) parallel and distributed computational components in primitive feature detection. In such a component-based framework, any primitive component can be replaced by a new one while other components remain unchanged; (3) incremental information integration; (4) high level context information as additional information sources, which can be combined with bottom-up processing at any stage. This dissertation presents the basic principles, criteria, and techniques for detector design and hypothesis verification based on the statistical detection and decision theory. In addition, evidence fusion strategies were investigated in this dissertation. Several novel detection algorithms and evidence fusion methods were proposed and their effectiveness was justified in automatic speech recognition and broadcast news video segmentation system. We believe such a detection-based framework can be employed in more applications in the future.Ph.D.Committee Chair: Lee, Chin-Hui; Committee Member: Clements, Mark; Committee Member: Ghovanloo, Maysam; Committee Member: Romberg, Justin; Committee Member: Yuan, Min

Towards Formal Structural Representation of Spoken Language: An Evolving Transformation System (ETS) Approach

Speech recognition has been a very active area of research over the past twenty years. Despite an evident progress, it is generally agreed by the practitioners of the field that performance of the current speech recognition systems is rather suboptimal and new approaches are needed. The motivation behind the undertaken research is an observation that the notion of representation of objects and concepts that once was considered to be central in the early days of pattern recognition, has been largely marginalised by the advent of statistical approaches. As a consequence of a predominantly statistical approach to speech recognition problem, due to the numeric, feature vector-based, nature of representation, the classes inductively discovered from real data using decision-theoretic techniques have little meaning outside the statistical framework. This is because decision surfaces or probability distributions are difficult to analyse linguistically. Because of the later limitation it is doubtful that the gap between speech recognition and linguistic research can be bridged by the numeric representations. This thesis investigates an alternative, structural, approach to spoken language representation and categorisation. The approach pursued in this thesis is based on a consistent program, known as the Evolving Transformation System (ETS), motivated by the development and clarification of the concept of structural representation in pattern recognition and artificial intelligence from both theoretical and applied points of view. This thesis consists of two parts. In the first part of this thesis, a similarity-based approach to structural representation of speech is presented. First, a linguistically well-motivated structural representation of phones based on distinctive phonological features recovered from speech is proposed. The representation consists of string templates representing phones together with a similarity measure. The set of phonological templates together with a similarity measure defines a symbolic metric space. Representation and ETS-inspired categorisation in the symbolic metric spaces corresponding to the phonological structural representation are then investigated by constructing appropriate symbolic space classifiers and evaluating them on a standard corpus of read speech. In addition, similarity-based isometric transition from phonological symbolic metric spaces to the corresponding non-Euclidean vector spaces is investigated. Second part of this thesis deals with the formal approach to structural representation of spoken language. Unlike the approach adopted in the first part of this thesis, the representation developed in the second part is based on the mathematical language of the ETS formalism. This formalism has been specifically developed for structural modelling of dynamic processes. In particular, it allows the representation of both objects and classes in a uniform event-based hierarchical framework. In this thesis, the latter property of the formalism allows the adoption of a more physiologically-concreteapproach to structural representation. The proposed representation is based on gestural structures and encapsulates speech processes at the articulatory level. Algorithms for deriving the articulatory structures from the data are presented and evaluated

Cortical encoding and decoding models of speech production

To speak is to dynamically orchestrate the movements of the articulators (jaw, tongue, lips, and larynx), which in turn generate speech sounds. It is an amazing mental and motor feat that is controlled by the brain and is fundamental for communication. Technology that could translate brain signals into speech would be transformative for people who are unable to communicate as a result of neurological impairments. This work first investigates how articulator movements that underlie natural speech production are represented in the brain. Building upon this, this work also presents a neural decoder that can synthesize audible speech from brain signals. Data to support these results were from direct cortical recordings of the human sensorimotor cortex while participants spoke natural sentences. Neural activity at individual electrodes encoded a diversity of articulatory kinematic trajectories (AKTs), each revealing coordinated articulator movements towards specific vocal tract shapes. The neural decoder was designed to leverage the kinematic trajectories encoded in the sensorimotor cortex which enhanced performance even with limited data. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

Dealing with linguistic mismatches for automatic speech recognition

Recent breakthroughs in automatic speech recognition (ASR) have resulted in a word error rate (WER) on par with human transcribers on the English Switchboard benchmark. However, dealing with linguistic mismatches between the training and testing data is still a significant challenge that remains unsolved. Under the monolingual environment, it is well-known that the performance of ASR systems degrades significantly when presented with the speech from speakers with different accents, dialects, and speaking styles than those encountered during system training. Under the multi-lingual environment, ASR systems trained on a source language achieve even worse performance when tested on another target language because of mismatches in terms of the number of phonemes, lexical ambiguity, and power of phonotactic constraints provided by phone-level n-grams. In order to address the issues of linguistic mismatches for current ASR systems, my dissertation investigates both knowledge-gnostic and knowledge-agnostic solutions. In the first part, classic theories relevant to acoustics and articulatory phonetics that present capability of being transferred across a dialect continuum from local dialects to another standardized language are re-visited. Experiments demonstrate the potentials that acoustic correlates in the vicinity of landmarks could help to build a bridge for dealing with mismatches across difference local or global varieties in a dialect continuum. In the second part, we design an end-to-end acoustic modeling approach based on connectionist temporal classification loss and propose to link the training of acoustics and accent altogether in a manner similar to the learning process in human speech perception. This joint model not only performed well on ASR with multiple accents but also boosted accuracies of accent identification task in comparison to separately-trained models

Fractal based speech recognition and synthesis

Transmitting a linguistic message is most often the primary purpose of speech com­munication and the recognition of this message by machine that would be most useful. This research consists of two major parts. The first part presents a novel and promis­ing approach for estimating the degree of recognition of speech phonemes and makes use of a new set of features based fractals. The main methods of computing the frac­tal dimension of speech signals are reviewed and a new speaker-independent speech recognition system developed at De Montfort University is described in detail. Fi­nally, a Least Square Method as well as a novel Neural Network algorithm is employed to derive the recognition performance of the speech data. The second part of this work studies the synthesis of speech words, which is based mainly on the fractal dimension to create natural sounding speech. The work shows that by careful use of the fractal dimension together with the phase of the speech signal to ensure consistent intonation contours, natural-sounding speech synthesis is achievable with word level speech. In order to extend the flexibility of this framework, we focused on the filtering and the compression of the phase to maintain and produce natural sounding speech. A ‘naturalness level’ is achieved as a result of the fractal characteristic used in the synthesis process. Finally, a novel speech synthesis system based on fractals developed at De Montfort University is discussed. Throughout our research simulation experiments were performed on continuous speech data available from the Texas Instrument Massachusetts institute of technology ( TIMIT) database, which is designed to provide the speech research community with a standarised corpus for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition system