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

Spectral discontinuity in concatenative speech synthesis – perception, join costs and feature transformations

This thesis explores the problem of determining an objective measure to represent human perception of spectral discontinuity in concatenative speech synthesis. Such measures are used as join costs to quantify the compatibility of speech units for concatenation in unit selection synthesis. No previous study has reported a spectral measure that satisfactorily correlates with human perception of discontinuity. An analysis of the limitations of existing measures and our understanding of the human auditory system were used to guide the strategies adopted to advance a solution to this problem. A listening experiment was conducted using a database of concatenated speech with results indicating the perceived continuity of each concatenation. The results of this experiment were used to correlate proposed measures of spectral continuity with the perceptual results. A number of standard speech parametrisations and distance measures were tested as measures of spectral continuity and analysed to identify their limitations. Time-frequency resolution was found to limit the performance of standard speech parametrisations.As a solution to this problem, measures of continuity based on the wavelet transform were proposed and tested, as wavelets offer superior time-frequency resolution to standard spectral measures. A further limitation of standard speech parametrisations is that they are typically computed from the magnitude spectrum. However, the auditory system combines information relating to the magnitude spectrum, phase spectrum and spectral dynamics. The potential of phase and spectral dynamics as measures of spectral continuity were investigated. One widely adopted approach to detecting discontinuities is to compute the Euclidean distance between feature vectors about the join in concatenated speech. The detection of an auditory event, such as the detection of a discontinuity, involves processing high up the auditory pathway in the central auditory system. The basic Euclidean distance cannot model such behaviour. A study was conducted to investigate feature transformations with sufficient processing complexity to mimic high level auditory processing. Neural networks and principal component analysis were investigated as feature transformations. Wavelet based measures were found to outperform all measures of continuity based on standard speech parametrisations. Phase and spectral dynamics based measures were found to correlate with human perception of discontinuity in the test database, although neither measure was found to contribute a significant increase in performance when combined with standard measures of continuity. Neural network feature transformations were found to significantly outperform all other measures tested in this study, producing correlations with perceptual results in excess of 90%

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Application of singing synthesis techniquest to bertsolaritza

237 p

Automatic voice disorder recognition using acoustic amplitude modulation features

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 114-117).An automatic dysphonia recognition system is designed that exploits amplitude modulations (AM) in voice using biologically-inspired models. This system recognizes general dysphonia and four subclasses: hyperfunction, A-P squeezing, paralysis, and vocal fold lesions. The models developed represent processing in the auditory system at the level of the cochlea, auditory nerve, and inferior colliculus. Recognition experiments using dysphonic sentence data obtained from the Kay Elemetrics Disordered Voice Database suggest that our system provides complementary information to state-of-the-art mel-cepstral features. A model for analyzing AM in dysphonic speech is also developed from a traditional communications engineering perspective. Through a case study of seven disordered voices, we show that different AM patterns occur in different frequency bands. This perspective challenges current dysphonia analysis methods that analyze AM in the time-domain signal.by Nicolas Malyska.S.M