## 1 Introduction  
Gas-insulated switchgear (GIS) is a metal switchgear which encloses circuit breakers, isolating switches, earthing switches, transformers, surge arresters, busbars, and other electrical devices [1; 2]. Since it was invented, GIS has been one of the most important and widely used types of power equipment in the power grid [3]. However, the occurrence of insulation defects, which are generated in the process of production, transportation, and maintenance, results in potential risks of serious accidents [4]. Various methods such as measurement and evaluation of return voltage, tan\(\delta\), partial discharge (PD), insulation resistance, as well as polarization and depolarization current have been studied and applied to condition monitoring on insulations of high voltage equipment in order to avoid serious accidents [5; 6; 7; 8]. As partial discharge is the most prominent indicator of insulation degradation in GIS [9; 10], researchers have presented a variety of techniques to detect PD signals in GIS for insulation monitoring, such as UHF (ultra-high frequency) detection, ultrasonic detection, optical detection, and chemical detection [11; 12; 13; 14]. As ultrasonic detection is a noninvasive and nondestructive measurement method and it processes high sensitivity, as well as strong resistance to electromagnetic interference, it has been increasingly used for defect diagnosis of GIS in normal operation [11; 15]. The traditional diagnostic technique for GIS, based on ultrasonic detection, is to analyze the peak value, RMS, 50 Hz correlation, and 100 Hz correlation of the detected PD signals according to the experience of testers, resulting in unstable accuracy [15; 16; 17]. Otherwise, the methods based on the analysis of waveform features and wavelet transform are also applied for defect diagnosis of power equipment [18; 19; 20]. [PERSON] et al. [18] extracted waveform features (rise time, pulse width, amplitude, energy, and ringing) from the first detected PD pulse and used a probabilistic neural network to diagnose the defects of transformers. [PERSON] et al. [19] used wavelet transform and singular value decomposition to extract features from PD signals and adopted artificial neural network to identify the types of insulation defects in XLPE cable. However, as the waveform features cannot discriminate the defects of floating electrode and creeping discharge precisely [18] and the wavelet transform is unable to conduct analysis on the whole frequency of signals [21], these methods still have limitations to obtain high accuracy in defect identification. This paper presents a classification system based on the technology of voiceprint recognition to automatically and precisely identify the insulation defects of GIS. Voiceprint recognition was invented and originally applied to speaker identification based on the acoustic features of speech signals [22]. Due to its significant advantages including low cost, less complexity, and high accuracy [23; 24], the application of voiceprint recognition technology has been extended to the engineering and medical fields and has achieved positive results. [PERSON] et al. [25] adopted voiceprint recognition technology to investigate the characteristics of the transformer noise. [PERSON] et al. [26] tried to use the technology to diagnose the faults in axial piston pumps and the effect of this method was verified by experience results [26]. The technology has also been applied to emboli detection based on ultrasonic signals, which has achieved great classification accuracy of 83.04% [27]. Acoustic feature extraction and pattern recognition are the key to voiceprint recognition technology [28; 29; 30]. The most common acoustic features include mel frequency cepstrum coefficients (MFCC), linear prediction cepstrum coefficients (LPCC), and perceptual linear prediction [30]. Among these acoustic features, the outstanding advantages of MFCC include simple extraction method, high accuracy, and strong resistance to noise [31].