Faculty of Information Technology, BUT

Publication Details

Autoregressive Modeling based Feature Extraction for Aurora3 DSR Task

MOTLÍČEK Petr and ČERNOCKÝ Jan. Autoregressive Modeling based Feature Extraction for Aurora3 DSR Task. In: Proc. EUROSPEECH 2003. Geneva: Institute for Perceptual Artificial Intelligence, 2003, pp. 1801-1804. ISSN 1018-4074.
Czech title
Extrakce příznaků založena na autoregresivních modelech pro AURORA3 DSR úlohu
Type
conference paper
Language
english
Authors
URL
Keywords
speech proceesing, speech recognition, all-pole model, PLP, MFCC, feature extraction
Abstract
Autoregressive Modeling based Feature Extraction for Aurora3 DSR Task
Annotation
Techniques for analysis of speech, that use autoregressive (all-pole) modeling approaches, are presented here and compared to generally known Mel-frequency cepstrum based feature extraction. In the paper, first, we focus on several possible applications of modeling speech power spectra that increase the performance of ASR system mainly in case of large mismatch between training and testing data. Then, the attention is payed to the different types of features that can be extracted from all-pole model to reduce the overall word error rate. The results show that generally used cepstrum based features, which can be easily extracted from all-pole model, are not the most suitable parameters for ASR, where the input speech is corrupted by different types of real noises. Very good recognition performances were achieved e.g., with discrete or selective all-pole modeling based approaches, or with decorrelated line spectral frequencies. The feature extraction techniques were tested on SpeechDat-Car databases used for front-end evaluation of advanced distributed speech recognition (DSR) systems.
Published
2003
Pages
1801-1804
Journal
European Speech Communication, vol. 2003, no. 9, ISSN 1018-4074
Proceedings
Proc. EUROSPEECH 2003
Conference
Eurospeech 2003-Switzerland - 8th European conference on speech communication and technology, Geneva, CH
Publisher
Institute for Perceptual Artificial Intelligence
Place
Geneva, CH
BibTeX
@INPROCEEDINGS{FITPUB7233,
   author = "Petr Motl\'{i}\v{c}ek and Jan \v{C}ernock\'{y}",
   title = "Autoregressive Modeling based Feature Extraction for Aurora3 DSR Task",
   pages = "1801--1804",
   booktitle = "Proc. EUROSPEECH 2003",
   journal = "European Speech Communication",
   volume = 2003,
   number = 9,
   year = 2003,
   location = "Geneva, CH",
   publisher = "Institute for Perceptual Artificial Intelligence",
   ISSN = "1018-4074",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/7233"
}
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