# Target Speaker Extraction by Fusing Voiceprint Features
## 1 Introduction
However, in some low-resource conditions such as embedded platforms, these computationally heavy models are not feasible. The mel-scale frequency cepstral coefficients (MFCCs) are a set of acoustic features which are often used in the tasks of speech recognition [17], speaker verification [18], sound classification [19; 20], etc. They contain sufficient information about the speaker's voice characteristics, such as energies in different frequency regions, and are regarded as voiceprint features for speaker identification and verification. With the development of deep learning techniques, the deep speaker embeddings (e.g., i-vector, d-vector, and x-vector) have been proposed. The i-vector [21] is extracted from the bottleneck layer of a DNN and is widely used in the speaker verification task [22; 23] along with a probabilistic linear discriminant analysis (PLDA) [24] scoring model. The d-vector [25; 26] is one of the DNN-based embeddings designed for the speaker verification task. It is extracted from the log-mel filter-bank features of the audio by employing a maxout DNN. The d-vector features are generated by averaging the last hidden layer activation of the model. The x-vector [27] is an important evolution of the d-vector, which is extracted using a time-delayed neural network [28] and a statistical pooling operation. It has demonstrated its effectiveness in many speaker recognition applications [29; 30; 31; 32; 33]. Considering that MFCCs and other widely used voiceprint features can be easily obtained, they can be directly applied to target speaker extraction tasks. However, to our knowledge, it has not been fully discussed how to effectively integrate the existing voiceprint features into the speaker extraction models. Therefore, in this paper, we propose three different voiceprint feature fusion methods and explore the effectiveness of fused features in the task of target speaker extraction. Specifically, two types of voiceprint features, i.e., MFCCs and d-vector, are fused with the magnitude spectrograms which were originally used in the speaker-independent speech separation models. Besides, three feature fusion methods are proposed for merging the features of voiceprint and magnitude spectrograms. These methods are uniformly named Tse-FV (short for target speaker extraction by fusing voiceprint features) model. Our contributions are summarized as follows:  
* Two types of voiceprint features, i.e., MFCCs and d-vector, are explored and their effectiveness in performance enhancement when integrated into the speaker-independentspeech separation models is investigated. Moreover, the d-vector features originally used by VoiceFilter are substituted for MFCC features for a performance comparison. * Three different feature fusion methods are proposed to fuse the voiceprint features with the magnitude spectrograms: (1) direct concatenation (Tse-FV(DC)); (2) expanded convolution and concatenation (Tse-FV(ECC)); and (3) concatenation and expanded convolution (Tse-FV(CEC)). * A feature integration method which efficiently integrates the voiceprint features into two speaker-independent speech separation models, i.e., DPCL and PITNet, is proposed. Experimental results are presented to corroborate the effectiveness and computational efficiency of the proposed Tse-FV model.