###### Abstract  
The characterization of voice quality is important for the diagnosis of a voice disorder. Vocal Fry is a voice quality which is traditionally characterized by a low frequency and a long closed phase of the glottis. However, we also observed amplitude modulated vocal Fry glottal area waveforms (GAWs) without long closed phases (positive group) which we modelled using an analysis-by-synthesis approach. Natural and synthetic GAWs are modelled. The negative group consists of euphonic, i.e., normophonic GAWs. The analysis-by-synthesis approach fits two modelled GAWs for each of the input GAW. One modelled GAW is modulated to replicate the amplitude and frequency modulations of the input GAW and the other modelled GAW is unmodulated. The modelling errors of the two modelled GAWs are determined to classify the GAWs into the positive and the negative groups using a simple support vector machine (SVM) classifier with a linear kernel. The modelling errors of all vocal Fry GAWs obtained using the modulating model are smaller than the modelling errors obtained using the unmodulated model. Using the two modelling errors as predictors for classification, no false positives or false negatives are obtained. To further distinguish the subtypes of amplitude modulated vocal Fry GAWs, the entropy of the modulator's power spectral density and the modulator-to-carrier frequency ratio are obtained. v 202  
and evidence against classifying vocal fry as pathological is provided. The results were further supported by an investigation of vocal fry with 104 first year graduate students [11]. Of the students in the study group, 86% had a nonpathological voice and the remaining 14% were reported to use vocal fry along with a pathological voice. However, 16% of the students with nonpathological voice quality also used vocal fry. For the objective detection of vocal fry and creak, these following acoustic features have been used in the past. The presence of vocal fry segments in speech utterances was detected based on the autocorrelation properties of the audio signals [3]. The insertion and detection rates obtained were 13% and 74%, respectively. In [12], audio features like inter-frame periodicity, interpulse similarity, peak fall and peak rise, H2-H1, i.e., the difference in amplitudes of the first two harmonics, F0 contours in each frame and peak prominence were used for vocal fry detection. An aperiodicity, periodicity and pitch (APP) detector, which uses a dip profile of the average magnitude difference function (AMDF) at lags, was used to distinguish between irregular phonation and modal phonation [13]. A Fourier spectrum analysis approach of the audio signals was also proposed for distinguishing vocal fry segments from diplophonic voice [14]. The ratio of the numbers of harmonics present in consecutive frames was determined to detect transitions of modal to vocal fry regimes, or vice versa. In total, 81% of the voice segments were correctly detected as vocal fry. The distinct characteristics of the creaky and modal regions were obtained using so called epoch parameters [15]. Epochs are negative to positive zero crossing instances of zero frequency filtered audio signals. The used epoch parameters were the number of epochs in a frame, the time instants of epochs, and the time interval between successive epochs. Based on the variance of these epoch parameters, a neural network classifier was developed to identify creaky regions. This study was done using voice samples of several speaker groups. The F1 scores were analyzed to validate the detector. The maximum F1 score was found to be a little over 0.8 for female Finnish speakers when compared to the other groups. Convolutional neural networks (CNN) based on the detection of the creaky voice from emergency calls was also proposed, where 32 mel-scale log filter-bank outputs were used as input features [16]. The data consist of emergency call recordings and a conversational speech corpus with 30 recordings each.