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TORRES-CARRASQUILLO P A , SINGER E , KOHLER M A , et al . Approaches to language identification using Gaussian mixture models and shifted delta cepstral features [C ] //Proceedings of the 7th International Confrence on Spoken Language Processing. Piscataway, NJ , US : IEEE , 2002 : 89 - 92 .

HEMANSK H . Perceptual linear predictive (PLP) analysis of speech [J ] . The Journal of the Acoustical Society of America , 1990 , 87 ( 4 ): 1738 - 1752 . DOI: 10.1121/1.399423 http://doi.org/10.1121/1.399423 https://pubs.aip.org/jasa/article/87/4/1738/930759/Perceptual-linear-predictive-PLP-analysis-of https://pubs.aip.org/jasa/article/87/4/1738/930759/Perceptual-linear-predictive-PLP-analysis-of A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, is presented and examined. This technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical-band spectral resolution, (2) the equal-loudness curve, and (3) the intensity-loudness power law. The auditory spectrum is then approximated by an autoregressive all-pole model. A 5th-order all-pole model is effective in suppressing speaker-dependent details of the auditory spectrum. In comparison with conventional linear predictive (LP) analysis, PLP analysis is more consistent with human hearing. The effective second formant F2′ and the 3.5-Bark spectral-peak integration theories of vowel perception are well accounted for. PLP analysis is computationally efficient and yields a low-dimensional representation of speech. These properties are found to be useful in speaker-independent automatic-speech recognition.

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JIN M , SONG Y , MCLOUGHLIN I , et al . LID-senones and their statistics for language identification [J ] . IEEEACM Transactions on Audio, Speech, and Language Processing , 2019 , 26 ( 1 ): 171 - 183 .

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DESHWAL D , SANGWAN P , KUMAR D . Feature extraction methods in language identification: a survey [J ] . Wireless Personal Communications , 2019 , 107 ( 4 ): 2071 - 2103 . DOI: 10.1007/s11277-019-06373-3 http://doi.org/10.1007/s11277-019-06373-3 Language Identification (LI) is one of the widely emerging field in the areas of speech processing to accurately identify the language from the data base based on some features of the speech signal. LI technologies have a wide set of applications in different spheres due to the growing advancement in the field of artificial intelligence and machine learning. Feature extraction is one of the fundamental and significant process performed in LI. This review presents main paradigms of research in Feature Extraction methods that will provide a deep insight to the researchers about the feature extraction techniques for future studies in LI. Broadly, this review summarizes and compare various feature extraction approaches with and without noise compensation techniques as the current trend is towards robust universal Language Identification framework. This paper categorizes the different feature extraction approaches on the basis of different features, human speech production system/peripheral auditory system, spectral or cepstral analysis, and lastly on the basis of transform. Moreover, the different noise compensation-based feature extraction techniques are also covered in the review. This paper also presents, that Mel-Frequency Cepstral Coefficients (MFCCs) are the most popular approach. Results indicates that MFCC fused with other feature vectors and cleansing approaches gives improved performance as compared to the pure MFCC based Feature Extraction approaches. This study also describes the different categories at the front end of the LI system from research point of view.

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刘威 . 单通道语音水印与语音增强算法研究 [D ] . 南京 : 东南大学 , 2017 .

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马元锋 , 陈克安 , 马苗 , 等 . 一种新的可应用于声目标识别的倒谱系数 [J ] . 兵工学报 , 2009 , 30 ( 11 ): 1477 - 1483 . 提出一种新的倒谱系数，与目前广泛采用的美尔倒谱系数( MFCC)相比有以下改进：1）基于听觉外周模型改进了美尔频率倒谱系数( MFCC)的三角滤波器；2）用与频率相关的指数压缩代替固定的对数压缩；3）分析了频率的临界带变换在语音识别中所起的作用，从声目标识别的角度提出在临界带变换中引入与信号相关的自适应机制。通过4组声目标识别仿真对比试验，表明了新的倒谱系数比MFCC在抗噪声能力方面有明显提升。

MA Y F , CHEN K A , MA M , et al . A new cepstral coefficient applicable to sound target recognition [J ] . Acta Armamentarii , 2009 , 30 ( 11 ): 1477 - 1483 . (in Chinese)

冯红波 , 李萍 , 李波 . 基于自动色阶和多尺度Retinex彩色图像增强算法 [J ] . 无线电工程 , 2019 , 49 ( 10 ): 911 - 914 .

FENG H B , LI P , LI B . Based on automatic color scale and multi-scale Retinex color image enhancement algorithm [J ] . Radio Engineering , 2019 , 49 ( 10 ): 911 - 914 . (in Chinese)

ZHU D , HUANG M , YANG J J , et al . Identificationof spoken language from webcast using deep convolutionalrecurrent neural networks [C ] //Proceedings of 2019 International Conference on Information Technology. Sanya , China : IEEE , 2019 : 1147 - 1152 .

邵玉斌 , 刘晶 , 龙华 , 等 . 基于声道频谱参数的语种识别 [J ] . 北京邮电大学学报 , 2021 , 44 ( 3 ): 112 - 119 . DOI: 10.13190/j.jbupt.2020-228 http://doi.org/10.13190/j.jbupt.2020-228 针对低信噪比下语种识别正确率低的问题，提出了一种声道冲激响应频谱参数和Teager能量算子倒谱参数融合的识别方法.根据语音中不同特征信息量分布特性，首先在特征提取前端引入低通滤波器滤除信号高频部分，并采用重采样方法降低采样率，再基于信号频谱提取声道冲激响应频谱参数，然后融合Teager能量算子倒谱参数，最后通过高斯混合通用背景模型进行语种识别验证.不同信噪比条件下性能测试表明，所提方法相对于基于单一的梅尔频率倒谱系数特征、单一的伽玛通频率倒谱系数特征和基于对数梅尔尺度滤波器组能量特征，在低信噪比下提升约15 dB，显著提高了识别正确率.

SHAOYB , LIU J , LONG H , et al . Language recognition based on vocal tract spectral parameters [J ] . Journal of Beijing University of Posts and Telecommunications , 2021 , 44 ( 3 ): 112 - 119 . (in Chinese)