Journal ArticleDOI
Continuous speech recognition via centisecond acoustic states
TLDR
When trained to the voice of a particular speaker, the decoder recognized seven‐digit telephone numbers correctly 96% of the time, with a better than 99% per‐digit accuracy.Abstract:
Continuous speech was treated as if produced by a finite‐state machine making a transition every centisecond. The observable output from state transitions was considered to be a power spectrum—a probabilistic function of the target state of each transition. Using this model, observed sequences of power spectra from real speech were decoded as sequences of acoustic states by means of the Viterbi trellis algorithm. The finite‐state machine used as a representation of the speech source was composed of machines representing words, combined according to a “language model.” When trained to the voice of a particular speaker, the decoder recognized seven‐digit telephone numbers correctly 96% of the time, with a better than 99% per‐digit accuracy. Results for other tests of the system, including syllable and phoneme recognition, will also be given.read more
Citations
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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
A Maximum Likelihood Approach to Continuous Speech Recognition
TL;DR: This paper describes a number of statistical models for use in speech recognition, with special attention to determining the parameters for such models from sparse data, and describes two decoding methods appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks.
Journal ArticleDOI
Hidden Markov models for speech recognition
TL;DR: The role of statistical methods in this powerful technology as applied to speech recognition is addressed and a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations are discussed.
Book
Connectionist Speech Recognition: A Hybrid Approach
Hervé Bourlard,Nelson Morgan +1 more
TL;DR: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance.
Journal ArticleDOI
Continuous speech recognition by statistical methods
TL;DR: Experimental results are presented that indicate the power of the methods and concern modeling of a speaker and of an acoustic processor, extraction of the models' statistical parameters and hypothesis search procedures and likelihood computations of linguistic decoding.