Human voice, which is the most beautiful instrument, is produced by a complicated mechanism. The quality of the voice is assessed by various techniques. Using voice analysis tools thereby providing an objective assessment of voice. These methods support the clinicians in the diagnosis of laryngeal pathologies associated with the auditory perception and also in deciding the treatment in patients. This paper aims at extraction of voice parameters from voice signals such as Fundamental Frequency (F0 Mean and Standard Deviation) Pitch Perturbation Quotient (Jiiter PPQ), Relative Average Perturbation (Jitter RAP), Amplitude Perturbation Quotient (Shimmer APQ), Pitch Variation (vF0) and Amplitude Variation (vAm) using a novel algorithm leading to an application named Ephphatha was compared with the existing software CSL (Computerized Speech Lab) using t-test analysis. The Bland-Altman plot was done showing the correlation and the limits of agreement between the measures. The outcomes got illustrate that there is no significant difference (p-value>0.05) in F0 and its variation measures. Amplitude and pitch perturbation measures are moderately (>0.01 p-value <0.05) to weakly (p-value<0.01) correlated between the programs. Thus Ephphatha would undoubtedly equip the speech pathologists and therapists with a non-invasive approach to confirm their perceptual observations.
Acoustic analysis plays a vital role in the study of human voice. Acoustic parameters such as Fundamental frequency (F0) can be derived directly from voices. It is one of the most proven parameters that determines the normality or abnormality of voice disorders in people. The Statistical measures of fundamental frequency, such as mean F0 and standard deviation F0 are suitable for clinical assessment of voices. In this study, these measures are computed using Robust Algorithm for Pitch Tracking (RAPT) and Dr. Speech algorithms. The common error that occurs in the fundamental frequency estimation algorithm is the octave error. The postprocessing method namely de-step filter is applied to rectify this error. This study uses Dr. Speech as the benchmark and evaluates the robustness of the RAPT algorithm for the original F0 contour and de-step filtered contour. The results indicate that the use of the filter gives significant improvement to the performance of the RAPT algorithm, thus making it suitable for clinical evaluation of the fundamental frequency.
Human voice, which is one's unique identification, is used for verbal communication, to create wonderful music through singing, and to express one-self. It is produced by a complicate mechanism which is subdivided into three main components. They are (1) the lungs, (2) the vocal folds within the larynx, and (3) the articulators. The voice can be affected by many disorders which are grouped into two classes such as organic and functional. Organic voice disorders are caused by the physical changes in the voice mechanism. It includes edema [1], vocal nodule which is caused due to the variation in vocal fold tissues [2], aging, which affects the structure of the larynx [3], and these impairments come under the category of structural voice disorder. On the other hand, neurogenic disorder includes vocal tremor [4], dysphonia [5], and paralysis of vocal fold [6], caused by the problems with the central nervous system. Functional voice disorder includes vocal fatigue [7], hyperkinetic dysphonia [8], diplophonia [9] and ventricular phonation [10].Human emotions, moods, and inflection also cause voice variation [11]. In order to make a diagnosis of these impairments, a voice analysis application provides the pathologists to visualize and manipulate the voice of the patients. The speech signals, which are obtained from the subjects, are analyzed using these applications. This includes the extraction of the objective vocal features such as fundamental frequency, harmonics-to-noise ratio, and the percentage of jitter and shimmer. In
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