英文文献

自动化专业的英文文献

Improved speech recognition method

for intelligent robot

1 Overview of speech recognition

Speech recognition has received more and more attention recently due to the important theoretical meaning and practical value [5 ]. Up to now, most speech recognition is based on conventional linear system theory, such as Hidden Markov Model (HMM) and Dynamic Time Warping(DTW) . With the deep study of speech recognition, it is found that speech signal is a complex nonlinear process. If the study of speech recognition wants to break through, nonlinear

-system theory method must be introduced to it. Recently, with the developmentof nonlinea-system theories such as artificial neural networks(ANN) , chaos and fractal, it is possible to apply these theories to speech recognition. Therefore, the study of this paper is based on ANN and chaos and fractal theories are introduced to process speech recognition.

Speech recognition is divided into two ways that are speaker dependent and speaker independent. Speaker dependent refers to the pronunciation model trained by a single person, the identification rate of the training person?sorders is high, while others’orders is in low identification rate or can’t be recognized. Speaker independent refers to the pronunciation model trained by persons of different age, sex and region, it can identify a group of persons’orders. Generally, speaker independent system ismorewidely used, since the user is not required to conduct the training. So extraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system.

Speech recognition can be viewed as a pattern recognition task, which includes training and recognition.Generally, speech signal can be viewed as a time sequence and characterized by the powerful hidden Markov model (HMM). Through the feature

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