# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 2 Related Works  
Speech extraction algorithms can be divided into single-channel speech extraction algorithms and multi-channel speech extraction algorithms [14,15] according to the number of microphones that are used to record the speakers. Single-channel speech extraction is usually solved with time-domain or frequency-domain methods while multi-channel speech extraction is solved using the methods for extracting coherent signals from different speakers. In our work, we mainly focus on the problems of single-channel speech extraction. With the rapid development of machine learning and artificial intelligence, the performance of deep-learning-based audio signal processing algorithms has been further improved. Speech extraction technology has also advanced thanks to the development of deep learning [16, 17, 18, 19, 20, 21, 22, 23, 24]. In most cases, target speech is extracted in the frequency domain. In these methods, networks obtain spectrograms of the target speech using short-time Fourier transforms(STFTs) and then generate spectrograms of the estimated speech. Other methods are based on the time domain, which mainly work by extracting the time domain features of target speech and can solve the problem of phase mismatch by using adaptive front-end and direct regression instead of STFTs. Deep clustering (DC) is a frequency-domain method that was proposed by [PERSON] [25] in 2016. In this method, the amplitude spectrogram features of (T, F) dimension mixed speech are mapped into a higher dimension (T, F, D) deep embedded feature space, i.e., each time-frequency unit (T, F) is mapped into a D-dimensional feature vector, which makes the mixed input features more distinguishable. The target of this method is to generate binary masks, which allocate the areas belonging to the target speech with a mask of 1 and the areas belonging to the other speech with a mask of 0. By multiplying the binary masks by the spectrograms of mixed speech, the network can cover the areas of noise on the spectrograms of mixed speech and obtain target speech from the mixed speech. Another technique is permutation invariant training(PIT). In 2017, [PERSON] [26] proposed the PIT method and applied it to the speech extraction task. This method selects the smallest mean square error (MSE) as the optimization target and effectively solves the problem of the permutation of the target and interference to find the best match for the desired target compared to DC. In 2021, [PERSON] [27] combined the traditional PIT method with long short-term memory (LSTM) and managed to improve the algorithm efficiency. The algorithm has a probabilistic optimization framework and solves the problem of the low efficiency of PIT by finding the best output label allocation. This method is significantly superior to traditional speech extraction methods that use the signal-to-distortion ratio(SDR) and source-to-interference ratio (SIR). In conclusion, the PIT algorithm provides a good training criterion for speaker-independent speech extraction to deal with the permutation and combination problems. In summary, the frequency-domain methods depend on the consistency of the outputs. These methods have multiple outputs, which make it difficult to define the target outputs of the network. Moreover, inverse short-time Fourier transforms (ISTFTs) with enhanced amplitude spectrogram and original mixed-phase spectrograms have certain impacts on speech extraction performance. To solve the problems with the frequency-domain methods, the time-domain methods are used. Conv-TasNet is one of the most common methods. In 2019, [PERSON] [28] proposed the convolution time-domain audio separation network (Conv-TasNet), which is superior to several time-frequency amplitude masks for dual-speaker speech extraction. This method build learnable front ends instead of STFTs, thereby generating features that are similar to those of a spectrogram.