An Efficient Speech Recognition Algorithm for Small Intelligent Electronic Devices

The speech recognition technology makes it possible for people to communicate with intelligent electronic devices. However, existing speech recognition algorithms are overly complex for small intelligent electronic devices (e.g., mini speakers, intelligent toys, intelligent remote controls, etc.). For this, an efficient speech recognition algorithm is proposed. Firstly, the Mel-scale Frequency Cepstral Coefficients (MFCC) is applied to extract features of voices. Secondly, the Support Vector Machines (SVM) is used to train speech classification models. Finally, a speech database is collected to validate the proposed algorithm. The speech database contains 500 audio files of 10 speech commands for an electric motor car driving assistant and 550 audio files of 11 speech commands for a intelligent remote control. The proposed method is evaluated via a 5-fold cross-validation, and experiments show that the propose method acquires 94.20% and 88.73% average accuracy rates for the electric motor car driving assistant and the intelligent remote control, respectively.