691 research outputs found

    A Tutorial on Speech Synthesis Models

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    For Speech Synthesis, the understanding of the physical and mathematical models of speech is essential. Hence, Speech Modeling is a large field, and is well documented in literature. The aim in this paper is to provide a background review of several speech models used in speech synthesis, specifically the Source Filter Model, Linear Prediction Model, Sinusoidal Model, and Harmonic/Noise Model. The most important models of speech signals will be described starting from the earlier ones up until the last ones, in order to highlight major improvements over these models. It would be desirable a parametric model of speech, that is relatively simple, flexible, high quality, and robust in re-synthesis. Emphasis will be given in Harmonic / Noise Model, since it seems to be more promising and robust model of speech. (C) 2015 The Authors. Published by Elsevier B.V

    Text-Independent Automatic Speaker Identification Using Partitioned Neural Networks

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    This dissertation introduces a binary partitioned approach to statistical pattern classification which is applied to talker identification using neural networks. In recent years artificial neural networks have been shown to work exceptionally well for small but difficult pattern classification tasks. However, their application to large tasks (i.e., having more than ten to 20 categories) is limited by a dramatic increase in required training time. The time required to train a single network to perform N-way classification is nearly proportional to the exponential of N. In contrast, the binary partitioned approach requires training times on the order of N2. Besides partitioning, other related issues were investigated such as acoustic feature selection for speaker identification and neural network optimization. The binary partitioned approach was used to develop an automatic speaker identification system for 120 male and 130 female speakers of a standard speech data base. The system performs with 100% accuracy in a text-independent mode when trained with about nine to 14 seconds of speech and tested with six to eight seconds of speech

    Novel Pitch Detection Algorithm With Application to Speech Coding

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    This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions

    A Two-Phase Damped-Exponential Model for Speech Synthesis

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    It is well known that there is room for improvement in the resultant quality of speech synthesizers in use today. This research focuses on the improvement of speech synthesis by analyzing various models for speech signals. An improvement in synthesis quality will benefit any system incorporating speech synthesis. Many synthesizers in use today use linear predictive coding (LPC) techniques and only use one set of vocal tract parameters per analysis frame or pitch period for pitch-synchronous synthesizers. This work is motivated by the two-phase analysis-synthesis model proposed by Krishnamurthy. In lieu of electroglottograph data for vocal tract model transition point determination, this work estimates this point directly from the speech signal. The work then evaluates the potential of the two-phase damped-exponential model for synthetic speech quality improvement. LPC and damped-exponential models are used for synthesis. Statistical analysis of data collected in a subjective listening test indicates a statistically significant improvement (at the 0.05 significance level) in quality using this two-phase damped-exponential model over single-phase LPC, single-phase damped-exponential and two-phase LPC for the speakers, sentences, and model orders used. This subjective test shows the potential for quality improvement of synthesized speech and supports the need for further research and testing

    A Perceptual Subspace Approach for Modeling of Speech and Audio Signals With Damped Sinusoids

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    Signal Processing and Restoration

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    Statistical signal processing for echo signals from ultrasound linear and nonlinear scatterers

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    Single-Microphone Speech Separation: The use of Speech Models

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