document: Application of time-frequency principal component analysis to text-independent speaker identification We propose a formalism , called vector filtering of spectral trajectories , that allows the integration of a number of speech parameterization approaches -LRB- cepstral analysis , Delta and Delta Delta parameterizations , auto-regressive vector modeling , ... -RRB- under a common formalism . We then propose a new filtering , called contextual principal components -LRB- CPC -RRB- or time-frequency principal components -LRB- TFPC -RRB- . This filtering consists in extracting the principal components of the contextual covariance matrix , which is the covariance matrix of a sequence of vectors expanded by their context . We apply this new filtering in the framework of closed-set speaker identification , using a subset of the POLYCOST database . When using speaker-dependent TFPC filters , our results show a relative improvement of approximately 20 % compared to the use of the classical cepstral coefficients augmented by their Delta - coefficients , which is significantly better with a 90 % confidence level
doc_bio_tags: O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I
extractive_keyphrases: vector filtering spectral trajectories speech parameterization cepstral analysis contextual principal components contextual covariance matrix polycost database cepstral coefficients confidence level
abstractive_keyphrases: time-frequency principal component analysis text-independent speaker identification delta delta parameterization delta parameterization auto-regressive vector modeling closed-set speaker identification delta -coefficients