Cinematic techniques for speech processing: temporal decomposition and multivariate linear prediction
- 1 January 1992
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (15206149) , 153-156 vol.1
- https://doi.org/10.1109/icassp.1992.225949
Abstract
Two models, the temporal decomposition and the multivariate linear prediction, of the spectral evolution of speech signals capable of processing some aspects of the speech variability are presented. A series of acoustic-phonetic decoding experiments, characterized by the use of spectral targets of the temporal decomposition techniques and a speaker-dependent mode, gives good results compared to a reference system (i.e., 70% vs. 60% for the first choice). Using the original method developed by Laforia, a series of text-independent speaker recognition experiments, characterized by a long-term multivariate auto-regressive modelization, gives first-rate results (i.e., 98.4% recognition rate for 420 speakers) without using more than one sentence. Taking into account the interpretation of the models, these results show how interesting the cinematic models are for obtaining a reduced variability of the speech signal representation.Keywords
This publication has 7 references indexed in Scilit:
- Efficient coding of LPC parameters by temporal decompositionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Hierarchical AR model for time varying speech signalsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Temporal decomposition and acoustic-phonetic decoding of speechPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Standard and target driven AR-vector models for speech analysis and speaker recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Minimum prediction residual principle applied to speech recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1975
- The String-to-String Correction ProblemJournal of the ACM, 1974
- On the Fitting of Multivariate Autoregressions, and the Approximate Canonical Factorization of a Spectral Density MatrixBiometrika, 1963