Titlebook: Robustness in Automatic Speech Recognition; Fundamentals and App Jean-Claude Junqua,Jean-Paul Haton Book 1996 Springer Science+Business Med

宿醉

钢盔

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Nature and Perception of Speech Soundshen, the basic structure of the auditory system and the main mechanisms influencing speech perception are briefly described. Throughout this chapter, we also emphasize the influence of noise on speech production and perception. By introducing basic characteristics of speech sounds and how they are p

antenna

Background on Speech Analysisods currently in use in speech processing, such as the Fourier transforms and the linear prediction analysis. Then, we review typical parameter sets generally proposed to encode the speech signal prior recognition. While these methods give a reasonable representation of speech spectra, they do not p

NUDGE

Fundamentals of Automatic Speech Recognitiontorical review. We proceed by introducing popular distance measures used to evaluate the differences between extracted parameters. Then, we focus on the main pattern recognition approaches, namely dynamic programming algorithms, stochastic modeling, and neural networks. We conclude this chapter by r

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Speaker Variability and Specificityction about the influence of stress and emotion on the speech communication process. We proceed by introducing the variations dues to intra- and inter-speaker variability as well as the ones caused by the environment or the linguistic context. Finally, we briefly explore the advances made in finding

PATHY

Dealing with Noisy Speech and Channel Distortionsand the gap between machine and human performance, we review early studies and recent results about speech perception of distorted speech by human listeners. Finally, we focus on two important issues often neglected in the building of ASR systems: endpoint detection and the Lombard reflex.

abduction

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Towards Robust Speech Analysison and improved speech quality at the first processing stage of a speech recognizer, namely the speech acquisition level. Then, three auditory models, especially successful in noise conditions, are detailed. Together with their description, their application to ASR is also summarized. Finally, the f

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