# Perceptual Characteristics of Voice Identification in Noisy Environments
## 1 Introduction  
Voices constitute an important method of human communication. Voice identity recognition, a significant part of forensic science, is a process conducted to compare the voice characteristics of an unknown voice and a reference template from a potential suspect to determine the speaker's identity [1]. Under forensically realistic conditions, the diversity of case types and the complexity of the scene will cause interindividual variability [2], e.g., with respect to environmental factors [3], protective facial coverings [4], accent and language [5], etc. Interindividual variability affects the performance of voice identification [6]. A method by which to enhance and guarantee the performance of voice identification in a forensically realistic environment is an urgent issue that must be solved. In real forensic scenes, both the questioned recordings and suspect recordings contain different types and extents of background noise, which correspond to widespread interindividual variability [7]. The ubiquitous noise masks or submerges the voice, changing the auditory, spectral, and other acoustic features. These changes increase the challenge of acquiring the target speaker's characteristics for speech processing, thereby reducing theperformance of voice identity recognition [8]. Some methods have been extensively used to compensate mismatched voice identification in noisy environments, such as a multiscale chaotic feature [9], signal enhancement [10], the model compensation method [11], and score normalization [12]. However, such methods rely heavily on the prior information from the noise source or require a large number of data sets. Some aspects of the human voice are more suitable for auditory analysis (i.e., hearing). Auditorily based voice identification is an essential method for overcoming problems with small datasets and channel mismatches [13]. [PERSON] found that the order of anti-noise interference ability is as follows: hearing > formant > tone curve > intensity curve [14]. [PERSON] et al. conducted a listening examination and compared its result with an automatic speaker recognition system in the context of forensic analyses. They concluded that the auditory analysis was superior to the automatic baseline system for mismatched conditions. In addition, they noted higher-level perceptual information for speaker discrimination [15]. Therefore, human hearing is more robust to noisy environments and can better identify a target voice in the degraded state of noise interference. Even in complex acoustic scenes, the human auditory system can focus on the speaker's voice [16]. The auditory analysis in complex environments is related to two major characteristics of the auditory system: the time-frequency analysis characteristics of voice signals [17] and the masking effect of the human ear [18]. Physicist [PERSON] put forward the idea that the human ear was a spectrum analyzer over 100 years ago. The stapes' movement affects the cochlea's fluid pressure when sound travels from the outer ear to the middle ear. It causes the traveling wave to propagate along the basement membrane. The basement membrane has frequency selectivity, whereby different frequencies have different effects on auditory perception; for example, the human ear's perception of loudness changes with a changing frequency [19]. [PERSON] found that the intelligibility of a transmission system with the same bandwidth gradually decreased as the center frequency deviated to both ends [20]. [PERSON] et al. determined that the gain of different frequencies leads to different levels of intelligibility improvement [21]. [PERSON] et al. discovered that band-pass noise with spectral masking increased reaction times, whereas band-pass noise with a center frequency greater than birdsong tended to decrease response times [22]. The interference of noise on perception is caused by the distortion of the voice in the relevant frequency band.