Open AccessBook
Discrete-Time Processing of Speech Signals
TLDR
The preface to the IEEE Edition explains the background to speech production, coding, and quality assessment and introduces the Hidden Markov Model, the Artificial Neural Network, and Speech Enhancement.Abstract:
Preface to the IEEE Edition. Preface. Acronyms and Abbreviations. SIGNAL PROCESSING BACKGROUND. Propaedeutic. SPEECH PRODUCTION AND MODELLING. Fundamentals of Speech Science. Modeling Speech Production. ANALYSIS TECHNIQUES. Short--Term Processing of Speech. Linear Prediction Analysis. Cepstral Analysis. CODING, ENHANCEMENT AND QUALITY ASSESSMENT. Speech Coding and Synthesis. Speech Enhancement. Speech Quality Assessment. RECOGNITION. The Speech Recognition Problem. Dynamic Time Warping. The Hidden Markov Model. Language Modeling. The Artificial Neural Network. Index.read more
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Performance measurement in blind audio source separation
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The expectation-maximization algorithm
TL;DR: The EM (expectation-maximization) algorithm is ideally suited to problems of parameter estimation, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation.
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Wavelet-based statistical signal processing using hidden Markov models
TL;DR: A new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals is developed.
Journal ArticleDOI
Survey on speech emotion recognition: Features, classification schemes, and databases
TL;DR: A survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system, the choice of suitable features for speech representation, and the proper preparation of an emotional speech database for evaluating system performance are addressed.