## 1 Introduction  
Voice, as the most effective method of communication, is crucial to people's daily life. However, according to statistics, about 3-9% of people around the world have voice problems [1] due to the deterioration of air quality, overuse of voice, and so on. Therefore, more importance should be attached to the early diagnosis of vocal fold diseases. Over the past few decades, a number of machine learning methods have been proposed in the field of pathological voice detection and classification, such as support vector machine (SVM) [2, 3], hidden Markov model (HMM) [4], Gaussian mixture model (GMM) [5], Artificial Neural Network (ANN) [6], and so on. Meanwhile, by the method of combining signal processing and machine learning, pathological voice detection and classification has achieved encouraging results. [PERSON] et al. [7] combined glottal source features with Mel Frequency Cepstral Coefficients (MFCCs) and achieved a recognition rate of 74.32% in SVD. [PERSON] [8] believed that the glottal wave could better reflect the characteristics of the vocal cords than ordinary speech signals. Therefore, the features were extracted from glottal waves and combined with MPEG-7 features. The method finally achieved a recognition rate of 88.52% in SVD. [PERSON] et al. [9] proposed gammatone spectral latitude (GTSL) features, which could use a gammatone filter to simulate the auditory perception of the human ear. Finally, this feature achieved an average accuracy of 99.6%, 89.9%, and 97.4% in MEEL, SVD and HUPA databases, respectively. However, all this research has made a common assumption: the source data and target data are from the same database, in other words, the data from the source and target domain have the same distribution [10]. When the distribution changes, the recognition rate tends to drop [11]. Furthermore, in application, the change in distribution means the rebuild of the machine learning model, which is expensive and does not scale. Another fact is that the scales of the pathological voice database are usually small due to the expense of manual labeling. According to a survey [12], there are mainly three pathological voice databases used by researchers. Although the voice samples chosen from the databases are mostly sustained vowels /a/, the sharing of the available databases also suffers from a number of factors, such as different sampling frequencies, voice loudness, sex [13], types of recording situations, and so on. In contrast with traditional machine learning methods, transfer learning methods allow the source domain and target domain to have different distributions [14]. The aim of transfer learning is to eliminate the divergence among databases by feature transformation. Meanwhile, transfer learning has achieved great success in cross-domain recognition and classification problems, such as semantic segmentation [15], natural language processing [16], automatic speech recognition [17], and so on. Furthermore, various transfer learning methods have been proposed to deal with the mismatch between the source and target domain. From the point of geometric feature transformation method, [PERSON] [18] proposed a subspace alignment (SA) method. This method aimed to find a linear transformation matrix, which aligned the source subspace with the target one. Subspace distribution alignment (SDA) [19] was proposed, which was combined with conditional distribution adaptation based on subspace alignment. Different from SA and SDA, which only focus on the first-order statistics of the source and target distributions, CORrelation Alignment [20] minimizes the domain shift by aligning the second-order statistics of the source and target distributions. In [21], [PERSON] proposed weighted MMD and applied weighted MMD to the neural network. [PERSON] et al. [22; 23] proposed a new transfer learning method to reduce the marginal distribution across domains by constructing a least squares regression model. Recently, [PERSON] et al. [24] used nonnegative matrix factorization to select features from voice.