Incorporating MLP features in the unsupervised training process
نویسندگان
چکیده
The combined use of multi layer perceptron (MLP) and perceptual linear prediction (PLP) features has been reported to improve the performance of automatic speech recognition systems for many different languages and domains. However, MLP features have not yet been used on unsupervised acoustic model training. This approach is introduced in this paper with encouraging results. In addition, unsupervised language model training was also investigated for a Portuguese broadcast speech recognition task, leading to a slight improvement of performance. The joint use of the unsupervised techniques presented here leads to an absolute WER reduction up to 3.2% over a baseline unsupervised system.
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