A new method for determining the number of gaussians in hidden Markov models for continuos speech recognition systems / Metodo para a determinação do numero de gaussianas em modelos ocultos de Markov para sistemas de reconhecimento de fala continua

AUTOR(ES)
DATA DE PUBLICAÇÃO

2006

RESUMO

Nowadays, HMM-based speech recognition systems are used in many real time processing applications, from cell phones to auto mobile automation. In this context, one important aspect to be considered is the HMM complexity, which directly determines the system computational load. So, in order to make the system feasible for practical purposes, it is interesting to optimize the HMM size constrained to a minimum acceptable recognition performance. Furthermore, topology optimization is also important for reliable parameter estimation. Previous works in this area have used likelihood measures in order to obtain models with a better compromise between acoustic resolution and robustness. This work presents the new Gaussian Elimination Algorithm (GEA), which is based on a discriminative analysis and on an internal analysis, for determining the more suitable HMM complexity. The new approach is compared to the classical Bayesian Information Criterion (BIC), to an entropy based method, to a discriminative-based method for increasing the acoustic resolution of the HMMs and also to systems containing a fixed number of Gaussians per state

ASSUNTO(S)

model complexity modelos matematicos processos de gaussian elimination algorithm reconhecimento automatico da voz markov robustness hidden markov models algoritmos

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