An application of Speech Recognition use Markov Hidden Model on Controlling TV System
Date Issued
2012
Date
2012
Author(s)
Yang, Chun-Che
Abstract
Regardless of past, present or future, speech recognition system always plays an important role in scientific technology. It is very important to couple fast development of the electronic equipment and signal processing technology with the cloud concepts. Accordingly, the speech recognition is much useful than before. As regards to speech control, it means that machines can understand human’s languages. In this study, the speech recognition system is based on HMM (Hidden Markov Models) algorithm. Therefore, it is of course that the basic signal processing and MFCC (Mel-scale Frequency Cepstral Coefficient) are used by the framework of development speech recognition. Moreover, some speech models are used by Viterbi algorithm to identify the human speeches.
Specially, the speech signal involved must be processed to be more easily to use, and MFCC is used to extract the speech features. In order to achieve good features, the quality of the speech recording is the key point for recognition. This study is to describe an embedded system via speech control. In which, the Chinese speech recognition based on HMM is used in control systems and further the TV is controlled in a specific voice commands experiment. In the above experiment, it is divided into three architectures. Among them, the recognized speech is compared with the original one in the first structure, and consequently the recognition rate by 97.4% is obtained. In the second structure, non-learning speech is recognized to compare with the speech data base, in which the recognition rate by 92.4% is obtained. As regards to the third framework, specifically non-experimental speech is identified and controlled by importing the decision tree involved, so as to investigate whether the behavior to be required by the speech is achieved or not, in which the control accuracy rate by 96% is obtained.
The experimental results show that an appropriate selection on status-state numbers can get high recognition rate. On the contrary, too much or too less status- states will lead the lower recognition rate. Too many Gaussian mixtures cause complicated computation and consequently to drive the recognition rate no longer to increase. Finally in the study, it is to explore how to integrate speech recognition technology built and its related hardware control technology that is applied in the TV control.
Subjects
HMM
Viterbi
MFCC
Speech control
Speech recognition
Type
thesis
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