Fast unsupervised speak through a discriminative Eigen
نویسندگان
چکیده
We present a new method for unsupervised, fast speaker adaptation that combines the Eigen-MLLR transform approach with discriminative MLLR. We thereby aim to profit both from the performance improvements that are generally provided by a discriminative approach, and from the reliability that Eigen-MLLR has demonstrated in fast adaptation scenarios. We present first evaluation results on the Spoke 4 subset of the 1994 Wall Street Journal (WSJ) database. Our results show that, in fast enrollment scenarios, discriminative Eigen-MLLR allows for clear improvements both over non-discriminative Eigen-MLLR and over discriminative MLLR. We further introduce a method to estimate the weight parameters of Eigen-MLLR discriminatively, and show that this allows for further improvements on the considered data sets.
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