# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 1 Introduction  
With the development of information technology, intelligent service robots will play an important role in smart home systems. Auditory perception is one of the key technologies of intelligent service robots [1]. Research has shown that special attention is currently being given to human-robot interaction [2], and especially speech interaction in particular [3, 4]. It is clear that service robots are always working in noisy environments, and there are possible directional spatial interferences such as the competing speakers located in different locations, air conditioners, and so on. As a result, additive background noise and spatial interferences significantly deteriorate the quality and intelligibility of the target speech, and speech enhancement (SE) is considered the most important preprocessing technique for speech applications such as automatic speech recognition [5]. Single-channel SE and two-channel SE techniques have been studied for a long time, while practical applications have a number of constraints, such as limited physical space for installing large-sized microphones. The well-known single channel SE methods, including spectral subtraction, Wiener filtering, and their variations, are successful for suppressing additive background noise, but they are not able to suppress spatial interferences effectively [6]. Besides, mask-based SE methods have predominantly been applied in many SE and speech separation applications [7]. The key ideabehind mask-based SE methods is to estimate a spectrographic binary or soft mask to suppress the unwanted spectrogram components [7; 8; 9; 10; 11]. For binary mask-based SE methods, the spectrographic masks are \"hard binary masks\" where a spectral component is either set to 1 for the target speech component or set to 0 for the non-target speech component. Experimental results have shown that the performance of binary mask SE methods degrades with the decrease of the signal-to-noise ratio (SNR) and the masked spectral may cause the loss of speech components due to the harsh black or white binary conditions [7; 8]. To overcome this disadvantage, the soft mask-based SE methods have been developed [8]. In soft mask-based SE methods, each time-frequency component is assigned a probability linked to the target speech. Compared to the binary mask SE methods, the soft-mask SE methods have shown better capability to suppress the noise with the aid of some priori information. However, the priori information may vary with time, and obtaining the priori information is not an easy task. By further analyzing the mask-based SE algorithms, we have the following observations. (1) It is a challenging task to estimate a good binary spectrographic mask. When noise and competing speakers (speech interferences) exist, the speech enhanced by the estimated mask often suffers from the phenomenon of \"musical noise\". (2) The direction of arrival (DOA) of the target speech is considered as a known parameter for the target SE task. (3) A binaural microphone and an acoustic vector sensor (AVS) are considered as the most attractive front ends for speech applications due to their small physical size. For the AVS, its physical size is about 1-2 cm\({}^{3}\) and AVS also has the merits such as signal time alignment and a trigonometric relationship of signal amplitudes [12; 13; 14; 15; 16]. A high-resolution DOA estimation algorithm with a single AVS has been proposed by our team [12; 13; 14; 15; 16]. Some effort has also been made for the target SE task with one or two AVS sensors [17; 18; 19; 20; 21]. For example, with the minimum variance distortionless response (MVDR) criterion, [PERSON] et al. developed a beamforming method using the AVS [17]. Their experimental results showed that their proposed algorithm achieves good performance for suppressing noise, but brings certain distortion of the target speech.