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oai:recercat.cat:2117/887282025-07-17T06:21:01Zcom_2072_1033col_2072_452950

Optimization of speech parameter weighting for CDHMM word recognition Hernando Pericás, Francisco Javier Ayarte, J Monte Moreno, Enrique Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic Speech processing systems Processament de la parla Speech dynamic feature are routinely used in current speech recognition systems in combination with short-term (static) spectral features. The aim of this paper is to propose a method to automatically estimate the optimum ponderation of static and dynamic features in a speech recognition system. The recognition system considered in this paper is based on Continuous-Density Hidden Markov Modelling (CDHMM), widely used in speech recognition. Our approach consists basically in 1) adding two new parameters for each state of each model that weight both kinds of speech features, and 2) estimating those parameters by means of a discriminative training algorithm that minimizes the recognition error using the recently proposed Generalized Probabilistic Descent (GPD) method. Experimental results in speaker independent digit recognition show an important increase of recognition accuracy. Peer Reviewed Postprint (published version) 1995 Conference report Hernando, J., Ayarte, J., Monte, E. Optimization of speech parameter weighting for CDHMM word recognition. A: European Conference on Speech Communication and Technology. "EUROSPEECH '95: 4th European Conference on Speech Communication and Technology: Madrid, Spain: 18-21 September 1995". Madrid: 1995, p. 105-108. 1018-4074 https://hdl.handle.net/2117/88728 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access 4 p. application/pdf