2026-04-19T22:44:55Zhttps://recercat.cat/oai/requestoai:recercat.cat:2117/886932026-01-18T05:52:30Zcom_2072_1033col_2072_452950
RECERCAT
author
Hernando Pericás, Francisco Javier
author
Nadeu Camprubí, Climent
1994
The performance of the existing speech recognition systems degrades rapidly in the presence of background noise. The OSALPC (one-sided autocorrelation linear predictive coding) representation of the speech signal has shown to be attractive for speech recognition because of its simplicity and its high recognition performance with respect to the standard LPC in severe conditions of additive white noise. The aim of this paper is twofold: (1) to show that OSALPC also achieves good performance in a case of real noisy speech (in a car environment), and (2) to explore its combination with several robust similarity measuring techniques, showing that its performance improves by using cepstral liftering, dynamic features and multilabeling.Peer ReviewedPostprint (published version)
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
Automobiles
Markov processes
Automobiles
Cepstral analysis
Correlation methods
Hidden Markov models
Automòbils
Markov, Processos de
Speech recognition in noisy car environment based on OSALPC representation and robust similarity measuring techniques
Conference report