Discriminative speaker recognition using Large Margin GMM

International audienceMost state-of-the-art speaker recognition systems are based on discriminative learning approaches. On the other hand, generative Gaussian mixture models (GMM) have been widely used in speaker recognition during the last decades. In an earlier work, we proposed an algorithm for discriminative training of GMM with diagonal covariances under a large margin criterion. In this paper, we propose an improvement of this algorithm which has the major advantage of being computationally highly efficient, thus well suited to handle large scale databases. We also develop a new strategy to detect and handle the outliers that occur in the training data. To evaluate the performances of our new algorithm, we carry out full NIST speaker identification and verification tasks using NIST-SRE'2006 data, in a Symmetrical Factor Analysis compensation scheme. The results show that our system significantly outperforms the traditional discriminative Support Vector Machines (SVM) based system of SVM-GMM supervectors, in the two speaker recognition tasks

Different responses of northern and southern ecotypes of Betula pendula to exogenous ABA application

We investigated responses of northern and southern ecotypes of silver birch (Betula pendula Roth) to exogenous abscisic acid (ABA) under controlled environmental conditions to determine the role of ABA in cold acclimation and dormancy development. Abscisic acid was sprayed on the leaves and changes in freezing tolerance, determined by the electrolyte leakage test, and bud dormancy were monitored. Applied ABA induced cold acclimation but had no effect on growth cessation in seedlings grown in long day conditions (LD, 24-h photoperiod at 18 degreesC). It enhanced freezing tolerance and accelerated growth cessation in seedlings grown in short day conditions (SD, 12-h photoperiod at 18 degreesC), and slightly enhanced freezing tolerance in seedlings grown at low temperature (LT, 24-h photoperiod at 4 degreesC) in both ecotypes. There were distinct ecotypic differences in ABA-induced cold acclimation and dormancy development. The northern ecotype was more responsive to applied ABA than the southern ecotype, resulting in more rapid development of freezing tolerance in all treatments, and earlier dormancy development in SD. When plants were grown in a photoperiod just above the critical photoperiod for the ecotype (defined as the longest photoperiod that induces growth cessation), applied ABA caused growth cessation and dormancy development. Compared with ABA-treated seedlings grown in SD, dormancy development was delayed in ABA-treated seedlings exposed to a near-critical photoperiod, but even in this treatment dormancy developed faster in the northern ecotype than in the southern ecotype

Compensating Acoustic Mismatch Using Class-Based Histogram Equalization for Robust Speech Recognition

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating for an acoustic mismatch between training and test environments but also reducing the two fundamental limitations of the conventional histogram equalization method, the discrepancy between the phonetic distributions of training and test speech data, and the nonmonotonic transformation caused by the acoustic mismatch. The algorithm employs multiple class-specific reference and test cumulative distribution functions, classifies noisy test features into their corresponding classes, and equalizes the features by using their corresponding class reference and test distributions. The minimum mean-square error log-spectral amplitude (MMSE-LSA)-based speech enhancement is added just prior to the baseline feature extraction to reduce the corruption by additive noise. The experiments on the Aurora2 database proved the effectiveness of the proposed method by reducing relative errors by 62% over the mel-cepstral-based features and by 23% over the conventional histogram equalization method, respectively