Voice disability is a barrier of effective communication. Around 1.2% of the World’s population are facing some form of voice disability. Surgical procedures namely laryngoscopy, laryngeal electromyography, and stroboscopy are used for voice disability diagnosis. Researchers and practitioners have been working to find alternatives to these surgical procedures. Voice sample based diagnosis is one of them. The major steps followed by these works are (a) to extract voice features from voice samples and (b) to discriminate pathological voices from normal voices by using classifier algorithm. However, there is no consensus about the voice feature and classifier algorithm that can provide the best accuracy in screening voice disability. Moreover, some of the works use multiple voice features and multiple classifiers to ensure high reliability. In this paper, we address these issues. The motivation of the work is to address the need for non invasive signal processing technique to detect voice disability in general population. A comprehensive survey work is done related to voice disability detection methods. The paper contains two main parts. In the first part, we present background information including causes of voice disability, current procedures and practices, voice features, and classifiers. In the second part, we present a comprehensive survey work on voice disability detection algorithms. The issues and challenges related to the selection of voice feature and classifier algorithms have been addressed at the end of this paper.

This paper presents a pathological voice identification system employing signal processing techniques through cochlear implant models. The fundamentals of the biological process for speech perception are investigated to develop this technique. Two cochlear implant models are considered in this work: one uses a conventional bank of bandpass filters, and the other one uses a bank of optimized gammatone filters. The critical center frequencies of those filters are selected to mimic the human cochlear vibration patterns caused by audio signals. The proposed system processes the speech samples and applies a CNN for final pathological voice identification. The results show that the two proposed models adopting bandpass and gammatone filterbanks can discriminate the pathological voices from healthy ones, resulting in F1 scores of 77.6% and 78.7%, respectively, with speech samples. The obtained results of this work are also compared with those of other related published works.

This paper presents a convolutional neural network (CNN) based non-invasive pathological voice detection algorithm using signal processing approach. The proposed algorithm extracts an acoustic feature, called chromagram, from voice samples and applies this feature to the input of a CNN for classification. The main advantage of chromagram is that it can mimic the way humans perceive pitch in sounds and hence can be considered useful to detect dysphonic voices, as the pitch in the generated sounds varies depending on the pathological conditions. The simulation results show that classification accuracy of 85% can be achieved with the chromagram. A comparison of the performances for the proposed algorithm with those of other related works is also presented.

Breast cancer is one of the paramount causes of new cancer cases worldwide annually. It is a malignant neoplasm that develops in the breast cells. The early screening of this disease is essential to prevent its metastasis. A mammogram X-ray image is the most common screening tool practiced currently when this disease is suspected; all the breast lesions identified are not malignant. The invasive fine needle aspiration (FNA) of a breast mass sample is the secondary screening tool to clinically examine cancerous lesions. The visual image analysis of the stained aspirated sample imposes a challenge for the cytologist to identify the malignant cells accurately. The formulation of an artificial intelligence-based objective technique on top of the introspective assessment is essential to avoid misdiagnosis. This paper addresses several artificial intelligence (AI)-based techniques to diagnose breast cancer from the nuclear features of FNA samples. The Wisconsin Breast Cancer dataset (WBCD) from the UCI machine learning repository is applied for this investigation. Significant statistical parameters are measured to evaluate the performance of the proposed techniques. The best detection accuracy of 98.10% is achieved with a two-layer feed-forward neural network (FFNN). Finally, the developed algorithm’s performance is compared with some state-of-the-art works in the literature.

The spectral images provide the dynamic characteristics of the voice signal in the time and frequency domains. However, extracting the predominant spectral features from the voice samples is still challenging. This work generates cochleagram images to unveil detailed spectral content of the voice samples to recognize dysphonic voice. Both sustained vowel (‘/a/’) and sentence voice samples are considered to include phonation, respiration, and resonance of the vocal tone. Also, gender bias is eliminated by considering male and female voice samples separately, as they have structurally different vocal tracts, pharynx, and oral cavities. The simulation results show that the cochleagram, coined with a designed pre-trained convolutional neural network (CNN), can achieve 95% accuracy in identifying dysphonic voices with sentence samples. A robust, noninvasive, and automated voice pathology detection system is effectively generated through perceptual analysis of voice signals. The proposed automated pathological voice detection system can objectively correlate the clinical findings and assist in monitoring the treatment progress of dysphonic voice on top of subjective assessment by clinicians.