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This paper reports on an optimum dynamic progxamming DP based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical

Abstract-This paper reports on an optimum dynamic programming DP based time-normalization algorithm for spoken word recognition. First, ageneral principle of time-normalization is given using time- warping function.Then, two time-normalized distancedefinitions, d e d symmetric and asymmetric forms,are derived from the principle. These two forms are comparedwith each other through theoretical

An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.

The dynamic time warping algorithm is a dynamic programming algorithm and a very popular technique in speech recognition. Why is the DTW algorithm a suitable method for speech recognition?

Home Browse by Title Books Readings in speech recognition Dynamic programming algorithm optimization for spoken word recognition

In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper

In speech recognition systems that contain words, recognition requires a comparison between the input word and the various words in the dictionary. The effective solution of the problem lies in the dynamic comparison algorithms, the purpose of which is to introduce the time scales of two words into optimal correspondence.

The dynamic programming model can handle the problem of time axis distortion and the Bayesian neural network can solve the problem of spectral pattern variation in speech recognition.

This chapter gives an overview of the dynamic programming DP search strategy for large-vocabulary, continuous-speech recognition. Starting with the basic one-pass algorithm for word string recognition, we extend the search strategy to vocabularies of 20,000 words

This paper deels with the use of context-free grammars in automatic speech recognition. A dynamic programming algorithm for recognizing and parsing spoken word strings of a context-free grammar is presented. The algorithm can be viewed as a probabilistic extension of the CYK algorithm along with the incorporation of the nonlineer time alignment. Details of the implementation and experimental