Journal ArticleDOI
Towards increasing speech recognition error rates
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TLDR
In this article, the authors discuss some research directions for ASR that may not always yield an immediate and guaranteed decrease in error rate but which hold some promise for ultimately improving performance in the end applications, including discrimination between rival utterance models, the role of prior information in speech recognition, merging the language and acoustic models, feature extraction and temporal information, and decoding procedures reflecting human perceptual properties.About:
This article is published in Speech Communication.The article was published on 1996-05-01. It has received 182 citations till now. The article focuses on the topics: Word error rate.read more
Citations
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Journal ArticleDOI
Speech recognition by machines and humans
TL;DR: Comparisons suggest that the human-machine performance gap can be reduced by basic research on improving low-level acoustic-phonetic modeling, on improving robustness with noise and channel variability, and on more accurately modeling spontaneous speech.
MonographDOI
Text-to-Speech Synthesis
TL;DR: Text-to-Speech Synthesis provides an in-depth explanation of all aspects of current speech synthesis technology, and is designed for graduate students in electrical engineering, computer science, and linguistics.
Journal ArticleDOI
Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition
TL;DR: The results show that the hybrid system performed substantially better than source separation or missing data mask estimation at lower signal-to-noise ratios (SNRs), achieving up to 57.1% accuracy at SNR = -5 dB.
Journal ArticleDOI
Invited paper: Automatic speech recognition: History, methods and challenges
TL;DR: This tutorial examines the problem area, its methods, successes and failures, focusing on the nature of the speech signal and techniques to accomplish useful data reduction, and compares it with other areas of PR.
Journal ArticleDOI
Interacting with computers by voice: automatic speech recognition and synthesis
TL;DR: This paper examines how people communicate with computers using speech, and the popular mathematical model called the hidden Markov model (HMM) is examined; first-order HMMs are efficient but ignore long-range correlations in actual speech.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
S. Davis,Paul Mermelstein +1 more
TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.