On-line Bayesian speaker adaptation using tree-structured transformation and robust priors
نویسندگان
چکیده
This paper presents new results by using our recently proposed on-line Bayesian learning approach for affine transformation parameter estimation in speaker adaptation. The on-line Bayesian learning technique allows updating parameter estimates after each utterance and i t can accommodate flexible forms of transformation functions as well as prior probability density function. We show through experimental results the robustness of heavy tailed priors to mismatch in prior density estimation. We also show that by properly choosing the transformation matrices and depths of hierarchical trees, recognition performance improved significantly.
منابع مشابه
On-line Bayesian Tree-structured Transformation of Hidden Markov Models for Speaker Adaptation
This paper presents a new recursive Bayesian learning approach for transformation parameter estimation in speaker adaptation. Our goal is to incrementally transform (or adapt) the entire set of HMM parameters for a new speaker or new acoustic enviroment from a small amount of adaptation data. By establishing a clustering tree of HMM Gaus-sian mixture components, the nest aane transformation par...
متن کاملOn-line hierarchical transformation of hidden Markov models for speaker adaptation
This paper presents a novel framework of on-line hierarchical transformation of hidden Markov models (HMM’s) for speaker adaptation. Our aim is to incrementally transform (or adapt) all the HMM parameters to a new speaker even though part of HMM units are unseen in adaptation data. The transformation paradigm is formulated according to the approximate Bayesian estimate, which the prior statisti...
متن کاملOnline Bayesian tree-structured transformation of HMMs with optimal model selection for speaker adaptation
This paper presents a new recursive Bayesian learning approach for transformation parameter estimation in speaker adaptation. Our goal is to incrementally transform or adapt a set of hidden Markov model (HMM) parameters for a new speaker and gain large performance improvement from a small amount of adaptation data. By constructing a clustering tree of HMM Gaussian mixture components, the linear...
متن کاملEnhancing the robustness of Bayesian methods for text-independent automatic speaker verification
In this paper we present the main advances of the IRISA speech group from 2001 to 2004 in robust methods for Bayesian adaptation of speaker models and Bayesian decision. The probabilistic framework and the state-of-the-art Bayesian approach for automatic speaker verification are first recalled. We then describe two original contributions for robust Bayesian decision. The first one is a score no...
متن کاملFast adaptation using constrained affine transformations with hierarchical priors
In this paper we present an approach to transformation based model adaptation that combines a fast, closed form solution to the MAP estimation of our transforms with robust priors. The robust priors are found using the technique of hierarchical priors, and a closed form solution is achieved by choosing diagonally constrained affine transformations and a suitable family of prior distributions fo...
متن کامل