Clinical diagnosis of voice disorder and evaluation of therapy outcome heavily rely on accurate quantification of voice quality, which is closely tied to the physiology and function of the laryngeal mechanism. Considering the evaluation methodology of the voice, two main categories of auditory-perceptual assessment and acoustic analysis can be identified. This article presents a new approach for acoustic analysis of voice quality, which brings several advantages to the field. The proposed approach is non-parametric in the sense that it does not require the estimation of the fundamental frequency or spectral response of the vocal tract. This reduces the computational complexity of the measurement and reduces the possible errors due to inaccurate estimation of those parameters. Additionally, the method does not make any assumption about the phonetic context and hence has the potential to be applied to connected speech. The proposed method benefits from the multiresolution structure of the wavelet analysis for estimating the noisy component of a voice in the spectro-temporal domain. The informativeness of the estimated noise for voice quality distinction is examined based on different noise-quantification approaches. It is shown that deviation from the model of the human auditory system (HAS) leads to performance improvement. Through several analyses, it is argued that using models of HAS for quantification of the noise leads to significant loss of information relevant to voice quality. Findings from this article suggest that perception-based measures of voice quality are highly restricted in capturing important aspects of acoustic that could assist with voice quality distinctions. This characteristic is inherent to HAS and cannot be alleviated, highlighting a significant limitation of perception-based measures.

Calibration and higher‐order statistics are standard components of image steganalysis. However, these techniques have not yet found adequate attention in audio steganalysis. Specifically, most of current studies are either non‐calibrated or only based on noise removal. The goal of this study is to fill these gaps and to show that calibrated features based on re‐embedding technique improve performance of audio steganalysis. Furthermore, the authors show that least significant bit is the most sensitive bit plane to data hiding algorithms, and therefore it can be employed as a universal embedding method. The proposed features also benefit from an efficient model which is tailored to the needs for audio steganalysis and represent the maximum deviation from human auditory system. Performance of the proposed method is evaluated on a wide range of data hiding algorithms in both targeted and universal paradigms. The results show the effectiveness of the proposed method in detecting the finest traces of data hiding algorithms in very low embedding rates. The system detects Steghide at capacity of 0.06 bit per symbol with sensitivity of 98.6% (music) and 78.5% (speech). These figures are, respectively, 7.1% and 27.5% higher than the state‐of‐the‐art results based on R‐Mel‐frequency cepstral coefficient features.