Language Identification Method Based on Fusion Feature MGCC

To solve the issue of low accuracy of language identification in a noisy environment, a language identification method is proposed by combining Mel-scale frequency cepstral coefficients and Gammatone frequency cepstral coefficients. First, the Mel-scale frequency cepstral coefficients and Gammatone frequency cepstral coefficients of speech are extracted, and the feature dimensions are screened based on the language contribution. Then, the feature is mapped in the spatial coordinate system composed of the Mel domain-Gammatone domain to obtain the Mel Gammatone cepstral coefficients (MGCC). Finally, the fusion feature is input into the deep bottleneck network. The experimental results show that the identification accuracy and speed of the proposed method are much higher than those of the single acoustic feature and other features. The accuracy can reach 99. 38% in the clean corpus, and can still reach more than 89% under the -5 dB environment, which fully proves the effectiveness and robustness of the proposed method. © 2023 Beijing University of Posts and Telecommunications. All rights reserved.