Titlebook: Connectionist Speech Recognition; A Hybrid Approach Hervé A. Bourlard,Nelson Morgan Book 1994 Springer Science+Business Media New York 1994

公豬

Experimental Systemsp at ICSI (in Berkeley, CA), (and more particularly Chuck Wooters, Phil Kohn, and Steve Renais, currently at Cambridge), Hynek Hermansky of US West Advanced Technologies (Denver, CO), and more recently with Michael Cohen, Horacio Franco, and Victor Abrash of SRI (Stanford, CA). The results of this c

Palpitation

overrule

System Tradeoffswever, word accuracy is only one measure of a practical speech recognition technique. Any computational method requires resources in the form of storage and communication (memory) bandwidth, as well as the ability to do the required arithmetic. The number of parameters used for a particular techniqu

POINT

Training Hardware and Softwaren process. In particular, a speech recognizer using MLP-based approaches scales well with more phonetic categories for requirements of storage, memory bandwidth, and numerical computation. This is particularly true when one takes into account the parameter-sharing that occurs in the hidden layer(s)

值得尊敬

FEIGN

HMM/MLP and Predictive Modelsmputationally tractable. One of these assumptions is the observation independence of the acoustic vectors. Indeed, it is usually assumed that the probability that a particular acoustic vector is emitted at a given time only depends on the current state and the current acoustic vector. As a consequen

Anhydrous

Feature Extraction by MLPrn classification plays a crucial role, it is only part of the vast speech recognition task. In spite of the spectacular progress made over the last decade, unrestricted speech recognition is still out of reach, and it is suspected that part of the difficulty lies in the use of inappropriate feature

背信

commonsense

https://doi.org/10.1007/978-1-4020-6435-7For thirty years, . (ANNs) have been used for difficult problems in pattern recognition [Viglione, 1970]. Some of these problems, such as the pattern analysis of brain waves, have been characterized by a low signal-to-noise ratio; in some cases it was not even known what was signal and what was noise.

沖突

IntroductionFor thirty years, . (ANNs) have been used for difficult problems in pattern recognition [Viglione, 1970]. Some of these problems, such as the pattern analysis of brain waves, have been characterized by a low signal-to-noise ratio; in some cases it was not even known what was signal and what was noise.