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Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 88-91). Also available via the Internet.

Thesis (Ph. D.)--Carleton University, 1993. / Also available in electronic format on the Internet.

by Chiu Kim Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 103-108). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Speech production --- p.2 / Chapter 1.2 --- Low bit rate speech coding --- p.4 / Chapter Chapter 2 --- Speech analysis & synthesis --- p.8 / Chapter 2.1 --- Linear prediction of speech signal --- p.8 / Chapter 2.2 --- LPC vocoder --- p.11 / Chapter 2.2.1 --- Pitch and voiced/unvoiced decision --- p.11 / Chapter 2.2.2 --- Spectral envelope representation --- p.15 / Chapter 2.3 --- Excitation --- p.16 / Chapter 2.3.1 --- Regular pulse excitation and Multipulse excitation --- p.16 / Chapter 2.3.2 --- Coded excitation and vector sum excitation --- p.19 / Chapter 2.4 --- Multiband excitation --- p.22 / Chapter 2.5 --- Multiband excitation vocoder --- p.25 / Chapter Chapter 3 --- Dual-band and Quad-band excitation --- p.31 / Chapter 3.1 --- Dual-band excitation --- p.31 / Chapter 3.2 --- Quad-band excitation --- p.37 / Chapter 3.3 --- Parameters determination --- p.41 / Chapter 3.3.1 --- Pitch detection --- p.41 / Chapter 3.3.2 --- Voiced/unvoiced pattern generation --- p.43 / Chapter 3.4 --- Excitation generation --- p.47 / Chapter Chapter 4 --- A low bit rate Quad-Band Excitation LSP Vocoder --- p.51 / Chapter 4.1 --- Architecture of QBELSP vocoder --- p.51 / Chapter 4.2 --- Coding of excitation parameters --- p.58 / Chapter 4.2.1 --- Coding of pitch value --- p.58 / Chapter 4.2.2 --- Coding of voiced/unvoiced pattern --- p.60 / Chapter 4.3 --- Spectral envelope estimation and coding --- p.62 / Chapter 4.3.1 --- Spectral envelope & the gain value --- p.62 / Chapter 4.3.2 --- Line Spectral Pairs (LSP) --- p.63 / Chapter 4.3.3 --- Coding of LSP frequencies --- p.68 / Chapter 4.3.4 --- Coding of gain value --- p.77 / Chapter Chapter 5 --- Performance evaluation --- p.80 / Chapter 5.1 --- Spectral analysis --- p.80 / Chapter 5.2 --- Subjective listening test --- p.93 / Chapter 5.2.1 --- Mean Opinion Score (MOS) --- p.93 / Chapter 5.2.2 --- Diagnostic Rhyme Test (DRT) --- p.96 / Chapter Chapter 6 --- Conclusions and discussions --- p.99 / References --- p.103 / Appendix A Subroutine of pitch detection --- p.A-I - A-III / Appendix B Subroutine of voiced/unvoiced decision --- p.B-I - B-V / Appendix C Subroutine of LPC coefficients calculation using Durbin's recursive method --- p.C-I - C-II / Appendix D Subroutine of LSP calculation using Chebyshev Polynomials --- p.D-I - D-III / Appendix E Single syllable word pairs for Diagnostic Rhyme Test --- p.E-I

We investigate, improve and develop speech analysis techniques which can be used to enhance various speech processing systems, especially low bit rate or variable bit rate coding of speech. The coding technique based on the sinusoidal representation of speech is investigated and implemented. Based on this study of the sinusoidal model of speech, improved analysis techniques to determine voicing, pitch and spectral estimation are developed, as well as noise reduction technique. We investigate the properties and limitations of the spectral envelope estimation vocoder (SEEVOC). We generalize, optimize and improve the SEEVOC and also compare it with LP in the presence of noise. The properties and applications of morphological filters for speech analysis are investigated. We introduce and investigate a novel nonlinear spectral envelope estimation method based on morphological operations, which is found to be very robust against noise. This method is also compared with the SEEVOC method. A simple method for the optimum selection of the structuring set size without using prior pitch information is proposed for many purposes. The morphological approach is then used for a new pitch estimation method and for the general sinusoidal analysis of speech or audio. Many of the new methods are based on a novel systematic analysis of the peak features of signals, including the study of higher order peaks. We propose a novel peak feature algorithm, which measure the peak characteristics of speech signal in time domain, to be used for end point detection and segmentation of speech. This nonparametric algorithm is flexible, efficient and very robust in noise. Several simple voicing measures are proposed and used in a new speech classifier. The harmonic-plus-noise decomposition technique is improved and extended to give an alternative to the methods used in the sinusoidal analysis method. Its applications to pitch estimation, speech classification and noise reduction are investigated.

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Thesis (Ph. D.)--University of New South Wales, 2005. / Also available online.

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