Int J Speech Technol DOI 10.1007/s10772-014-9228-6 Manifold learning based speaker dependent dimension reduction for robust text independent speaker verification Davood Zabihzadeh · Mohammad H. Moattar Received: 12 September 2013 / Accepted: 6 February 2014 © Springer Science+Business Media New York 2014 Abstract Speaker verification has been studied widely from different points of view, including accuracy, robust- ness and being real-time. Recent studies have turned toward better feature stability and robustness. In this paper we study the effect of nonlinear manifold based dimensionality reduc- tion for feature robustness. Manifold learning is a popu- lar recent approach for nonlinear dimensionality reduction. Algorithms for this task are based on the idea that each data point may be described as a function of only a few parame- ters. Manifold learning algorithms attempt to uncover these parameters in order to find a low-dimensional representation of the data. From the manifold based dimension reduction approaches, we applied the widely used Isometric mapping (Isomap) algorithm. Since in the problem of speaker veri- fication, the input utterance is compared with the model of the claiming client, a speaker dependent feature transforma- tion would be beneficial for deciding on the identity of the speaker. Therefore, our first contribution is to use Isomap dimension reduction approach in the speaker dependent con- text and compare its performance with two other widely used approaches, namely principle component analysis and factor analysis. The other contribution of our work is to perform the nonlinear transformation in a speaker-dependent framework. We evaluated this approach in a GMM based speaker veri- fication framework using Tfarsdat Telephone speech dataset for different noises and SNRs and the evaluations have shown reliability and robustness even in low SNRs. The results also D. Zabihzadeh (B ) Department of Computer Engineering, Asrar Institute of Higher Education, Mashhad, Iran e-mail: d.zabihzadeh@gmail.com; d-zabihzadeh@asrar.ac.ir M. H. Moattar Department of Software Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran e-mail: moattar@mshdiau.ac.ir show better performance for the proposed Isomap approach compared to the other approaches. Keywords Noise robust speaker recognition · Text independent speaker verification · Dimension reduction · Manifold learning 1 Introduction This paper concerns about improving the robustness of text- independent speaker verification. It is known that any mis- match between the training and testing conditions decreases the accuracy of speaker recognition. The main focus of speaker recognition research has been to tackle this mis- match. It is possible to use generic noise suppression tech- niques to enhance the quality of the signal, however, enhance- ment techniques increase the computational load of speaker verification and it is more desirable to develop a robust fea- ture extraction approach. This paper intends to study the effect of speaker dependent nonlinear dimension reduction techniques in speaker verifi- cation performance improvement especially in noisy condi- tions. We suppose that this type of approaches can get the intrinsic layout of speech data and can make the feature vec- tor invariant against noisy condition. From the vast number of nonlinear approaches for dimension reduction, manifold based techniques are proposed in this paper. Manifold learn- ing is a popular recent approach for nonlinear dimension- ality reduction (Huo et al. 2007; Lee and Verleysen 2010). These algorithms for this task are based on the idea that we can describe the data points as a combination of fewer basis vectors. Manifold learning approaches are very diverse and each of them has some specific characteristics. From these 123