## 1 Introduction  
Voice quality describes a wide range of multifaceted perceptual characteristics of human voice [1]. One of these characteristics is the voice type. Breathy, modal and pressed voice have been viewed on a continuum paradigm of phonation in terms of vocal fold contact area and open quotient [2]. Electroglottalgraph waveforms have shown that breathy voice featured a small Vocal Fold (VF) contact area and a large open quotient, and thus implied a low laryngeal resistance [3]. Pressed voice displayed opposite trends [3]. Methods of voice type classification may broadly be subdivided into two categories: (1) subjective methods; and (2) objective methods. The perceptual judgement of voice quality by panels of listeners (as opposed to self-evaluation) is still considered the gold standard for clinical voice evaluation and monitoring [4; 5; 6; 7]. Recency effect and listener experience could confound an individual's perceptual ratings [8; 9]. Notably, as perceptual voice assessment tools are context-specific, clinicians have limited ability to provide long-term monitoring and evaluation of patient's voice quality outside the clinic. Long-term voice data acquisition and monitoring is known to be challenging to implement in occupational settings. Automatic voice type discrimination based on ambulatory device data is therefore needed. Objective methods involve the processing of dynamic signals obtained from sensors and the calculation of metrics using a vast array of signal processing schemes. The microphone has primarily been used in a vast majority of voice quality studies [10; 11; 12; 13]. Although microphones are convenient, easy to use in the field and have a large bandwidth, the captured voice signals are often contaminated by background noise, and they are distorted by reverberation in the surrounding spaces. The large bandwidth of the microphone provides a broad spectral range, but the associated speech intelligibility implies privacy disclosure, which may be of concern for long-term voice monitoring applications. The Neck Surface Accelerometer (NSA) offers a viable alternative to microphones for the capture of voice signals [14; 15; 16; 17; 18]. The NSA is a small sensor that measures the vibration acceleration in the direction normal to the neck surface. During speech, neck tissue vibrations are mainly produced by the transmission of structure-borne vibrations induced by the acoustic waves in the subglottal and supraglottal portions of the vocal tract to the neck skin surface. Data are acquired through a wearable recorder or a smartphone. The NSA has a good sensitivity and bandwidth, but the neck tissue tends to dissipate high frequency vibrations, and thereby acts as a low-pass filter. Consequently, the bandwidth of the NSA signals is less than 1.5 kHz. The NSA mounted on the below-glottis skin surface can hardly capture formant information, which is important for speech intelligibility. Thus, the NSA protects speakers' privacy. The NSA signals accurately convey the features of the voice source such as fundamental frequency (f0) and vocal amplitude [19]. A semi-empirical model was reported to correlate Surface Acceleration Level (SAL) with Sound Pressure Level (SPL) based on 27 participants' data [20]. Glottal source characteristics such as the Maximum Flow Declination Rate (MFDR) and Harmonic Richness Factor (HRF) have been estimated from NSA signals using subglottal inverse filtering [21]. A comparison between vocal function measures derived from an acoustic microphone and from am NSA has also shown that f0 and jitter are congruent (\(p>0.98\)), but shimmer is less correlated (\(p<0.7\)) [17]. The first level of objective voice evaluation and classification is normally based on statistical analysis on voice metrics extracted from recorded voice signals. A plethora of temporal, spectral and cepstral metrics have been proposed [2; 22; 23; 24]. Jitter and shimmer measures of fundamental frequency and loudness perturbations were found to correlate with voice quality based on microphone data [25].