Eigen-MLLRs applied to unsupervised speaker enrollment for large vocabulary continuous speech recognition

The concept of eigen-MLLRs (Kuan-ting Chen et al., Proc. ICSLP 2000, p.742-5, 2000; Wang, N. J.-C. et al., Proc. ICASSP 2001, p.345-9, 2001), a variant of the eigenvoice method, is applied to unsupervised speaker enrollment in a large vocabulary CSR system. The emphasis is on fast adaptation. Two ways of estimating multiple eigen-MLLR transformations are introduced, either joint or separated with respect to the eigen-MLLR vector space. The first case allows multiple transforms to be robustly estimated from sparse data while the second achieves more accurate adaptation when more samples become available. The first decoded words spoken by a new test speaker are used to adapt the speaker-independent HMM means. The impact of this new enrollment algorithm is evaluated over a large real-life database dealing with professional medical transcriptions. Significant reductions of word-error-rates are achieved with less than 10 seconds of enrollment speech and without any supervision.