Издательство Springer, 2007, -292 pp.
International Conference on Nonlinear Speech Processing, NOLISP 2007, Paris, France, May 22-25, 2007. Revised Selected Papers.
We present in this volume a collection of revised selected papers from the ISCA Tutorial and Research Workshop on Nonlinear Speech Processing (NOLISP 2007) held in Paris, France, 22–25 May, 2007. NOLISP 2007 was organized by the University Pierre and Marie Curie (UPMC) with the generous support of ISCA (International Speech Communication Association), EURASIP and the IEEE. NOLISP 2007 was the first follow-on workshop to a series of three earlier events related to nonlinear speech processing, that were organized within the framework of the European COST action 277 Nonlinear speech processing (2001–2005). The financial support of ISCA enabled the attendance of leading researchers from various parts of the world.
The exciting field of speech processing has witnessed tremendous development over the past 20 years or so, thanks to both technological progress and to the growing focus of research on a number of key application areas. However, some specificities of the speech signal are still not well addressed by the currently available models. Hence, new nonconventional models and processing techniques need to be investigated in order to foster and/or accompany future progress, even if they do not match immediately the level of performance and understanding of the current state-of-the-art approaches.
The purpose of NOLISP is to present and discuss novel ideas, work and results related to such alternative techniques for speech processing, which depart from mainstream approaches. It declared its intent to be an interdisciplinary forum, intertwining research in different fields of speech processing with its growing applications in everyday practice.
One of the special characteristics of the NOLISP volumes is that the authors usually propose new improvements for speech processing by drawing inspiration from other exciting fields including, amongst others, statistical signal processing, pattern classification, multi-modal processing, perceptual-based criteria, auditory processing and machine learning-based approaches.
Non-Linear and Non-Conventional TechniquesPhase-Based Methods for Voice Source Analysis
Some Experiments in Audio-Visual Speech Processing
Exploiting Nonlinearity in Adaptive Signal Processing
Speech SynthesisMixing HMM-Based Spanish Speech Synthesis with a CBR for Prosody Estimation
Objective and Subjective Evaluation of an Expressive Speech Corpus
Speaker RecognitionOn the Usefulness of Linear and Nonlinear Prediction Residual Signals for Speaker Recognition
Multi Filter Bank Approach for Speaker Verification Based on Genetic Algorithm
Speaker Recognition Via Nonlinear Phonetic- and Speaker-Discriminative Features
Perceptron-Based Class Verification
Speech RecognitionManifold Learning-Based Feature Transformation for Phone Classification
Word Recognition with a Hierarchical Neural Network
Hybrid Models for Automatic Speech Recognition: A Comparison of Classical ANN and Kernel Based Methods
Towards Phonetically-Driven Hidden Markov Models: Can We Incorporate Phonetic Landmarks in HMM-Based ASR?
A Hybrid Genetic-Neural Front-End Extension for Robust Speech Recognition over Telephone Lines
Efficient Viterbi Algorithms for Lexical Tree Based Models
Speech AnalysisNon-stationary Self-consistent Acoustic Objects as Atoms of Voiced Speech
The Hartley Phase Cepstrum as a Tool for Signal Analysis
Voiced Speech Analysis by Empirical Mode Decomposition
Estimation of Glottal Closure Instances from Speech Signals by Weighted Nonlinear Prediction
Quantitative Perceptual Separation of Two Kinds of Degradation in Speech Denoising Applications
Exploitation of non-linear techniquesAn Efficient VAD Based on a Generalized Gaussian PDF
Estimating the Dispersion of the Biometric Glottal Signature in Continuous Speech
Trajectory Mixture Density Networks with Multiple Mixtures for Acoustic-Articulatory Inversion
Application of Feature Subset Selection Based on Evolutionary Algorithms for Automatic Emotion Recognition in Speech