## 1 Introduction  
Due to the COVID-19 pandemic, masks are widely used to prevent the spread of the virus. Although the pandemic is fading, masks are still an integral part of our daily lives because of the constant mutation of virus strains [1]. However, wearing masks can impair automatic speech recognition (ASR), which is an important means of realizing human-machine interaction [2; 3; 4] in many fields, such as home automation [5], vehicle systems [6], and reception [7] and healthcare services [8]. Recent acoustic studies showed that masks work like a low-pass filter, attenuating frequencies above 1-2 kHz [9; 10; 11]. The transmission loss of the masks affects relevant frequency components of speech transmission and impairs speech intelligibility [11]. In addition, the visual cues obscured by masks also negatively affect human speech recognition [12]. The results of [PERSON] et al. showed that, compared to those made under unmasked conditions, listeners have a lower percentage of correct responses when listening to audio-only recordings made with masks, and authors suggested using a lower signal-to-noise ratio (SNR) in future studies to avoid the ceiling effect [13]. [PERSON] et al. found that the masks reduced Speech Transmission Index scores by 3%-45% [14]. [PERSON] et al. conducted speech recognition tests using audio-only recordings made with or without masks and found that masks negatively affected speech intelligibility (_p_ < 0.001) [9]. [PERSON] et al. studied the effects of facial masks on face-to-face speech recognition in real-worldnoisy environments. They found that speech recognition was poorer (\(p<0.001\)) when the talker wore a non-medical disposable mask [15]. However, other studies have given different results. [PERSON] et al. showed no impact of the paper or transparent face masks on the speech recognition performance of normal-hearing listeners in a four-talker babble background at +10 dB of SNR, with or without visual cues [16]. [PERSON] et al. found that the surgical mask did not influence speech understanding in a normal-hearing group [17]. These two studies were conducted before the COVID-19 pandemic and only tested the traditional medical mask (surgical mask) [16; 17]. The ceiling effect in the test of [PERSON] et al. with intelligibility higher than 96.9% in all conditions should also be considered, as [PERSON] et al. indicated [9]. It might be concluded that masks significantly affect English speech recognition at a low SNR, and until now, there has been no relevant research on ASR. Most prior research on the factors that affect ASR has focused on noise, speaker accent, speaker age, and multiple speakers. Noise persists as a significant hurdle to developing ASR, and many approaches have been proposed to enhance the robustness of ASR systems [18; 19]. Our previous study suggested a method to associate the Articulation Index to estimate the influence of stationary noise on the ASR word accuracy (ACC) [20]. [PERSON] et al. quantified the bias of a SOTA ASR system and found that the speech of native speakers is recognized much better than that of non-native speakers of Dutch [21]. [PERSON] et al. proposed a novel decoupling and interacting multi-task network to improve joint speech and accent recognition [22]. Accurate recognition of child and elderly speech remains challenging because of the inherent differences between such and adult voices [23; 24]. Multi-talker speech recognition, expected to identify \"who spoke what\", is also a complex problem [25]. GB/T 41813.1-2022 [26] provides general test items and a general testing method for speech recognition in intelligent speech-interaction applications. The effects of noise, speaker accent, speaker age, multiple speakers, etc., on ASR are considered in the GB/T 41813.1-2022 [26]. To date, the impact of masks as a factor that impairs ASR due to acoustic attenuation and alteration of the speech time-frequency characteristics, has not been considered. This study evaluated the effects of masks on ASR accuracy with or without noise.