Adapting PSN recognition models to the GSM environment by using spectral transformation

In this work, environment adaptation is studied in order to transform PSN speaker independent isolated words HMM to the GSM environment. Linear multiple regression (LMR) transformations associated with groups of HMM densities are used to adapt the densities. Both mean vectors and covariance matrices of the densities are adapted. It has been shown that a small amount of GSM data are sufficient to transform the PSN HMM in order to match the GSM environment and to achieve a performance equivalent to those of an HMM trained with a large amount of GSM data. The number of groups of Gaussian densities seems to have a small influence on the results. However, the minimum number of groups depends on the vocabulary size. Finally, this technique is compared to the Bayesian adaptation and the results show that similar performance can be obtained with both methods