# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
###### Abstract  
The extraction of a target speaker from mixtures of different speakers has attracted extensive amounts of attention and research. Previous studies have proposed several methods, such as SpeakerBeam, to tackle this speech extraction problem using clean speech from the target speaker to provide information. However, clean speech cannot be obtained immediately in most cases. In this study, we addressed this problem by extracting features from the electroglottographs (EGGs) of target speakers. An EGG is a laryngeal function detection technology that can detect the impedance and condition of vocal cords. Since EGGs have excellent anti-noise performance due to the collection method, they can be obtained in rather noisy environments. In order to obtain clean speech from target speakers out of the mixtures of different speakers, we utilized deep learning methods and used EGG signals as additional information to extract target speaker. In this way, we could extract target speaker from mixtures of different speakers without needing clean speech from the target speakers. According to the characteristics of the EGG signals, we developed an EGG_auxiliary network to train a speaker extraction model under the assumption that EGG signals carry information about speech signals. Additionally, we took the correlations between EGGs and speech signals in silent and unvoiced segments into consideration to develop a new network involving EGG preprocessing. We achieved improvements in the scale invariant signal-to-distortion ratio improvement (SISDRi) of 0.89 dB on the Chinese Dual-Mode Emotional Speech Database (CDESD) and 1.41 dB on the EMO-DB dataset. In addition, our methods solved the problem of poor performance with target speakers of the same gender and the different between the same gender situation and the problem of greatly reduced precision under the low SNR circumstances. speech extraction; SpeakerBeam; electroglottograph; pre-processing Article  
An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction  
[PERSON], [PERSON], Z.  
where X is the spectrogram of a signal and is decomposed into the matrix product of a base matrix W and activation matrix H. Non-negative matrix factorization (NMF) lets W and H be non-negative to obtain non-negative matrix factorization[6; 7], which can be used to obtain the basic spectral patterns of non-negative data. Another method is called computational auditory scene analysis (CASA) [8], which uses auditory grouping cues. The CASA method [9] models auditory signals and utilizes the similarities between the fundamental frequencies of speech signals. A method based on Bayesian inference rules was proposed in a study by [PERSON] [10]. This method describes the formation process of pure tone sequencing as \"auditory streaming\" and estimates auditory sequencing by processing prior probability using the Bayesian criterion. Another method based on neural computation represents auditory flows in terms of units of neurons and competition between auditory flows is realized by inhibitory connections between neurons. [PERSON] [11] developed a method that utilizes local and global inhibitory mechanisms to separate auditory flows. Another method by [PERSON] [12] is based on time coherence, which uses a prediction mechanism to promote competition among different groups. The methods mentioned above extract speaker by establishing mathematical models, so their performances are not applicable in more complex situations. Additionally, the accuracy of the extracted speech using traditional methods is far below that of the deep learning methods. In recent years, deep learning methods have achieved good results in speech extraction under challenging conditions such as non-stationary interference. There have been many studies on speech extraction using deep learning methods.