Improving the efficiency of automatic speech recognition by feature transformation and dimensionality reduction
نویسندگان
چکیده
In speech recognition systems, feature extraction can be achieved in two steps: parameter extraction and feature transformation. Feature transformation is an important step. It can concentrate the energy distributions of a speech signal onto fewer dimensions than those of parameter extraction and thus reduce the dimensionality of the system. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular feature transformation methods. This paper investigates their performances in dimensionality reduction tasks in continuous speech recognition systems. A new type of feature transformation, LP transformation, is proposed and its performance is compared to those of LDA and PCA transformations.
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