Exploration of sparse representation techniques in language recognition

Language recognition (LR) refers to the task of identifying the language being spoken in a speech utterance with the help of machines. Following the success of the i-vector representation in speaker recognition task, it is also noted to yield state-of-the-art performances in LR task. The i-vectors form a popular low-rank representation of speech utterances and are derived from the Gaussian mixture model (GMM) mean supervectors using factor analysis. The i-vector extraction involves a low-rank "total variability matrix" which is hypothesized to incorporate both language and the session/channel variabilities. The session/channel variabilities are compensated by appropriate techniques like linear discriminant analysis (LDA), within class covariance normalization (WCCN), etc. The concept of total variability space as a single space is an extension of the joint factor analysis (JFA), where the language and the session/channel variabilities are modeled separately. In the recent years, sparse representation classification (SRC) has been successfully exploited in many pattern recognition tasks including LR. The existing SRC based LR approaches use an exemplar dictionary created by concatenating the i-vectors corresponding to the training utterances. The i-vector representation for the test utterance are then sparse coded over the exemplar dictionary. Based on the indices of the non-zero coefficients in the sparse coded vector (s-vector), the language of the spoken test utterance is determined. With increase in the number of training examples being available, the size of the exemplar dictionary also increases, and the computational burden of sparse coding over such highly redundant dictionary becomes quite prohibitive. Towards addressing that challenge, this thesis explore various dictionary learning approaches for designing compact dictionaries for LR task. These compact dictionaries not only reduce the computational burden but also result in enhanced LR performances. For dictionary learning, the K-SVD, the D-KSVD, the LC-KSVD, and the online learning (OL) dictionary approaches employing l0-norm, l1-norm, and combined l1 and l2-norm regularizations TH-1998_08610209 are investigated. Though the i-vector approach is found to be very successful, it suffers from high computational complexity involved in its extraction as well as high memory requirement in the storage of the total variability matrix. For addressing that, an ensemble of random subspaces of GMM mean supervector employing LDA followed by WCCN based session/channel compensation in the subspace has been investigated. The proposed approach does not require any learning for creating the subspaces and has very low memory requirements. In typical LR applications, the number of languages involved is not very large. As a result of that, unlike speaker recognition task, the overall complexity of the JFA based system turns out to be lower than that of the i-vector approach. Exploiting this fact, the combination of the proposed ensemble approach with JFA is also explored. Furthermore, the JFA compensated maximum a-posteriori (MAP) point estimates (i.e., latent vector) are themselves used as the spoken utterance representations. Compared to the i-vector, the JFA latent vector has considerably lower dimensionality. The sparse coding algorithms are then applied to the JFA latent vector, and the performances are compared with the i-vector based LR system. We also proposed an ensemble of learned-exemplar (class-wise concatenation of the learned dictionary) approach in order to reduce the latency and to achieve some diversity gain. In this approach, multiple small-sized dictionaries are created, and each of the dictionaries is used for extracting the sparse representation of a particular test utterance. In this way, multiple s-vectors are obtained for that test utterance. The language-specific coefficients in each of the s-vectors are averaged to have multiple score vectors. Finally, these score vectors are used to decide the spoken language of an unknown utterance. The recently proposed bottleneck features based i-vector representation is also explored in the sparse representation framework.