Syllable Matching Algorithm with Spectral Peak Point Feature for Chinese Speech

Based on the spectral peak point characteristics of Chinese speech, this study proposes a syllable matching algorithm to improve the matching effect of Chinese speech syllables in noisy environments. First, a discrete cosine transform is used to extract the speech signal envelope spectrogram, and the human ear masking effect is used for spectral energy judgment to obtain the extreme value points of spectral energy in each frame. Then, the syllable signal is corresponded to a binary sequence by performing binary quantization in the logarithmic frequency range. Finally, the syllable matching result is determined based on the template comparison of the binary sequence. The results show that the proposed algorithm outperforms the conventional methods for matching syllables in the noiseless Chinese speech. Additionally, it has a high matching accuracy at low signal-to-noise ratios.