In this paper, we explore the use of Independent Component Analysis (ICA) and Principal Component Analysis (PCA) techniques to reduce the dimensionality of high-level LVCSR features and at the same time to enable modelling them with state-of-the-art techniques like Probabilistic Linear Discriminant Analysis or Pairwise Support Vector Machines (PSVM). The high-level features are the coefficients from Constrained Maximum-Likelihood Linear Regression (CMLLR) and Maximum-Likelihood Linear Regression (MLLR) transforms estimated in an Automatic Speech Recognition (ASR) system. We also compare a classical approach of modeling every speaker by a single SVM classifier with the recent state-of-the-art modelling techniques in Speaker Identification. We report performance of the systems and score-level combination with a current state-of-the-art acoustic i-vector system on the NIST SRE2010 dataset.

Independent Component Analysis and MLLR Transforms for Speaker Identification / Cumani, Sandro; Plchot, O.; Karafiát, M.. - STAMPA. - (2012), pp. 4365-4368. (Intervento presentato al convegno The 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)).

Independent Component Analysis and MLLR Transforms for Speaker Identification

CUMANI, SANDRO;
2012

Abstract

In this paper, we explore the use of Independent Component Analysis (ICA) and Principal Component Analysis (PCA) techniques to reduce the dimensionality of high-level LVCSR features and at the same time to enable modelling them with state-of-the-art techniques like Probabilistic Linear Discriminant Analysis or Pairwise Support Vector Machines (PSVM). The high-level features are the coefficients from Constrained Maximum-Likelihood Linear Regression (CMLLR) and Maximum-Likelihood Linear Regression (MLLR) transforms estimated in an Automatic Speech Recognition (ASR) system. We also compare a classical approach of modeling every speaker by a single SVM classifier with the recent state-of-the-art modelling techniques in Speaker Identification. We report performance of the systems and score-level combination with a current state-of-the-art acoustic i-vector system on the NIST SRE2010 dataset.
2012
9781467300445
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2496926
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