The speaker recognition task falls under the general problem of pattern classification. Speaker recognition as a pattern classification problem, its ultimate objective is design of a system that classifies the vector of features in different classes by partitioning the feature space into optimal speaker discriminative space. Linear Discriminant Analysis (LDA) is a feature extraction method that...

This paper addresses instantaneous speaker adaptation, based on feature-space maximum likelihood linear regression (fMLLR), in the context of an automatic transcription task. We investigate the use of fMLLR-based adaptation when the need of a preliminary decoding pass for a speech segment is removed, as sufficient statistics for adaptation parameter estimation are gathered with respect to a Gau...

This paper investigates the use of maximum likelihood linear regression (MLLR) for both speaker and environment adaptation. MLLR transforms the mean and variance parameters of a set of HMMs. In this paper a number of different types of linear transformations of the variances are examined including full, block diagonal, and diagonal transformation matrices. Experiments on large vocabulary speake...

We extend the input transformation approach for adapting hybrid connectionist speech recognizers to allow multiple transformations to be trained. Previous work has shown the efficacy of the linear input transformation approach for speaker adaptation [1][2][3], but has focused only on training global transformations. This approach is clearly suboptimal since it assumes that a single transformati...

The use of adaptation transforms common in speech recognition systems as features for speaker recognition is an appealing alternative approach to conventional short-term cepstral modelling of speaker characteristics. Recently, we have shown that it is possible to use transformation weights derived from adaptation techniques applied to the Multi Layer Perceptrons that form a connectionist speech...

Voice transformation is the process of transforming the characteristics of speech uttered by a source speaker, such that a listener would believe the speech was uttered by a target speaker. In this paper we address the problem of transforming voice quality. We do not attempt to transform prosody. Our system has two main parts corresponding to the two components of the source-filter model of spe...

This paper proposes two types of bilinear transformation spacebased speaker adaptation frameworks. In training session, transformation matrices for speakers are decomposed into the style factor for speakers’ characteristics and orthonormal basis of eigenvectors to control dimensionality of the canonical model by the singular value decomposition-based algorithm. In adaptation session, the style ...

Voice conversion, i.e. modification of a speech signal to sound as if spoken by a different speaker, finds its use in speech synthesis with a new voice without necessity of a new database. This paper introduces two new simple non-linear methods of frequency scale mapping for transformation of voice characteristics between male and female or childish. The frequency scale mapping methods were dev...

We describe our formulation of transformation enhanced data modeling used to develop a multi-grained data analysis approach to text independent speaker recognition. The broad goal is to address difficulties caused by sparse training and test data. First, our development of maximum likelihood transformation based recognition with diagonally constrained Gaussian mixture models is detailed. We giv...

This paper treats a linear transformation of word templates in a word recognition system. The object of the transformation, called LMR-transform, is to adapt the recogniser to a new acoustical environment. The transform is derived by linear regression on pairs of word utterances from two different acoustical environments. The use of the transform has been evaluated for recognition accuracy, spe...