Continuous Speech Recognition Using Dynamic Bayesian Networks : A Fast Decoding Algorithm - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Book Sections Year : 2004

Continuous Speech Recognition Using Dynamic Bayesian Networks : A Fast Decoding Algorithm

Abstract

State-of-the-art automatic speech recognition systems are based on probabilistic modeling of the speech signal using Hidden Markov Models (HMMs). Recent work has focused on the use of dynamic Bayesian networks (DBNs) framework to construct new acoustic models to overcome the limitations of HMM based systems. In this line of research we proposed a methodology to learn the conditional independence assertions of acoustic models based on structural learning of DBNs. In previous work, we evaluated this approach for simple isolated and connected digit recognition tasks. In this paper we evaluate our approach for a more complex task: continuous phoneme recognition. For this purpose, we propose a new decoding algorithm based on dynamic programming. The proposed algorithm decreases the computational complexity of decoding and hence enables the application of the approach to complex speech recognition tasks.
Not file

Dates and versions

inria-00100260 , version 1 (26-09-2006)

Identifiers

  • HAL Id : inria-00100260 , version 1

Cite

Murat Deviren, Khalid Daoudi. Continuous Speech Recognition Using Dynamic Bayesian Networks : A Fast Decoding Algorithm. Gamez, José and Moral, Serafin and Salmeron, Antonio. Advances in Bayesian Networks, Springer Physica Verlag, pp.289-307, 2004, Studies in Fuzziness and Soft Computing. ⟨inria-00100260⟩
140 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More