Speech recognition of specific two-word Chinese vocabulary by applying Fourier transform twice to the broad-band spectrogram

This paper illustrates a method to recognize the speech of specific two-word Chinese vocabulary by analyzing speech signals using a broad-band spectrogram after Fourier transform is applied to it twice. First, we analyze the broad-band spectrogram in the frequency domain and its corresponding voice characteristics in detail after applying Fourier transform twice. Then, binary width zoning column projection is carried out in the broad-band spectrogram frequency domain. The projection value is treated as the characteristic value of speech recognition feature and the support vector machine (SVM) is considered as the classifier for recognizing the speech of specific two-word Chinese vocabulary. A total of 1000 voice samples were used in the simulation. The results using this method show a remarkable recognition rate of 93.4%. The proposed method provides a new way for vocabulary recognition.