# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 2 Related Works
In 2021, [PERSON] [29] proposed the dual-path recurrent neural network (DPRNN), which breaks long audio clips into smaller chunks to optimize the recurrent neural network (RNN) in a deep model. The DPRNN significantly minimizes the model size compared to the time-domain audio separation network (TasNet) and enhances speech extraction performance. Although the studies mentioned above have obtained excellent results in dealing with speech extraction problems, the networks tend to be complicated and lack portability. Time-domain speech extraction methods can achieve good extraction effects, but they are calculated point by point, and the time-domain models tend to be complex and require expensive computation costs. SpeakerBeam and VoiceFilter [30] are examples of target speech extraction models that can be used in both the frequency domain and time domain using extra information from target speakers. SpeakerBeam uses a sequence summary network to generate spectrograms containing the features of the target speaker's speech while VoiceFilter concatenates spectrogram features and d-vector features, which are extracted from the last hidden layer of the deep neural network, to estimate the clean speech of target speakers. In 2018, [PERSON] [31] optimized this method using ASR technology and utilized predicted hidden Markov model (HMM)-state posteriors to improve the masks. In 2019, [PERSON] [32] also refined SpeakerBeam to train models using extra information about target speaker instead of training particular models for target speakers. This network utilizes information about target speakers from adapted speech [33], both for single-channel and multi-channel speech extraction and achieves better performance than former networks. SpeakerBeam has also been modified with an attention mechanism [34] to extract features from the additional information, which effectively improves the performance of the multimodal SpeakerBeam network [35]. Additionally, [PERSON] [36] proposed an implementation of SpeakerBeam in the time-domain, with auxiliary speaker information added to the network. However, all of the speech extraction algorithms mentioned above are based on the premise that all mixed speech can be separated to obtain clean speech from a target speaker, but it is difficult to achieve this in practical application. Therefore, we focused on finding other signals that could provide information about the target speech and eventually identified EGGs [37; 38; 39; 40], which were invented by [PERSON] [41]. EGGs are a kind of skin electrical signal that measure vocal cord vibrations during laryngeal vocalization. The acquisition of EGG signals is not susceptible to other noise or vibrations. Thanks to method of collecting EGG signals directly from people's throat, they can be obtained effectively in extremely noisy environments. In 2020, [PERSON] [42] utilized a deep neural network and EGGs to estimate glottal glosure instants(GCI), and optimized the method by using the analysis synthesis settings of real speech signals, which improved the final performance for glottal closure instants. In 2020, [PERSON] [43] proposed a measurement to test the reliability of EGGs, which shows the differences in the values of vowels between different genders or individuals. This method illustrates the possibility of EGGs being used in speech processing. The features of the EGG signal extraction module were proposed based on LSTM units [44] to replace SpeakerBeam's feature extraction network. This method works through voiced segment extraction, feature extraction, and F0 smoothing and achieves a 91.2% accuracy in the classification of EGGs. In 2022, [PERSON] [45] proposed a cross-modal emotion distillation model that uses fundamental frequencies from EGG signals to improve the emotion recognition accuracy of emotional databases. Since previous studies have proved that EGGs can improve performance for other acoustic tasks, we applied EGGs to speech extraction tasks to verify whether EGGs can improve speech extraction performance.