Unsupervised training scheme with non-stereo data for empirical feature vector compensation
نویسندگان
چکیده
In this paper, a novel training scheme based on unsupervised and non-stereo data is presented for Multi-Environment Modelbased LInear Normalization (MEMLIN) and MEMLIN with cross-probability model based on GMMs (MEMLIN-CPM). Both are data-driven feature vector normalization techniques which have been proved very effective in dynamic noisy acoustic environments. However, this kind of techniques usually requires stereo data in a previous training phase, which could be an important limitation in real situations. To compensate this drawback, we present an approach based on ML criterion and Vector Taylor Series (VTS). Experiments have been carried out with Spanish SpeechDat Car, reaching consistent improvements:48.7% and 61.9% when the novel training process is applied over MEMLIN and MEMLIN-CPM, respectively.
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