Application of CMLLR in narrow band wide band adapted systems

The amount of training data has a crucial effect on the accuracy of HMM based meeting recognition systems. Conversational telephone speech matches speech in meetings well. However it is naturally recorded with low bandwidth. In this paper we present a scheme that allows to transform wide-band meeting data into the same space for improved model training. The transformation into a joint space allows simpler and more efficient implementation of joint speaker adaptive training (SAT) as well as adaptation of statistics for heteroscedastic discriminant analysis (HLDA). Models are tested on the NIST RT’05 meeting evaluation where a relative reduction in word error rate of 4% was achieved. With the use of HLDA and SAT the improvement was retained.