Unsupervised data-driven feature vector normalization with acoustic model adaptation for robust speech recognition

  • Authors:

  • Luis Buera;Antonio Miguel;Óscar Saz;Alfonso Ortega;Eduardo Lleida

  • Affiliations:

  • Speech Technology Group, Toshiba Research Europe, Cambridge, UK and Department of Electronic Engineering and Communications, University of Zaragoza, Zaragoza, Spain;Department of Electronic Engineering and Communications, University of Zaragoza, Zaragoza, Spain;Department of Electronic Engineering and Communications, University of Zaragoza, Zaragoza, Spain;Department of Electronic Engineering and Communications, University of Zaragoza, Zaragoza, Spain;Department of Electronic Engineering and Communications, University of Zaragoza, Zaragoza, Spain

  • Venue:
  • IEEE Transactions on Audio, Speech, and Language Processing
  • Year:
  • 2010

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Abstract

In this paper, an unsupervised data-driven robust speech recognition approach is proposed based on a joint feature vector normalization and acoustic model adaptation. Feature vector normalization reduces the acoustic mismatch between training and testing conditions by mapping the feature vectors towards the training space. Model adaptation modifies the parameters of the acoustic models to match the test space. However, since neither is optimal, both approaches use an intermediate space between training and testing spaces to map either the feature vectors or acoustic models. The joint optimization of both approaches provides a common intermediate space with a better match between normalized feature vectors and adapted acoustic models. In this paper, feature vector normalization is based on a minimum mean square error (MMSE) criterion. A Class Dependent Multi-Environment Model LInear Normalization (CD-MEMLIN) based on two classes (silence/speech) with a Cross Probability Model (CD-MEMLIN-CPM) is used. CD-MEMLIN-CPM assumes that each class of clean and noisy spaces can be modeled with a Gaussian mixture model (GMM), training a linear transformation for each pair of Gaussians in an unsupervised data-driven training process. This feature vector normalization maps the recognition space feature vector to a normalized space. The acoustic model adaptation maps the training space to the normalized space by defining a set of linear transformations over an expanded HMM-state space, compensating for those degradations that the feature vector normalization is not able to model, like rotations. Experiments have been carried out with the Spanish SpeechDat Car database and Aurora 2 databases using both the standard Mel-frequency cepstral coefficient (MFCC) and advanced ETSI front-ends. Consistent improvements were reached for both corpora and front-ends. Using the standard MFCC front-end, a 92.08% average improvement on WER for Spanish SpeechDat Car and a 69.75% average improvement for clean condition evaluation of Aurora 2 was obtained, improving those results reached with ETSI advanced front-end (83.28% and 67.41%, respectively). Using the ETSI advanced front-end with the proposed solution, a 75.47% average improvement was obtained for the clean condition evaluation of Aurora 2 database.