International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
A Survey Paper on Detection of Voice Pathology Using Machine Learning Yashaswini D M1, Dr. Bharathi M2 1Student,
S J C Institute of Technology, chikballapur, Karnataka, India S J C Institute of Technology, chikballapur, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------years, particularly in voice pathology detection systems Abstract - Voice pathology detection systems can 2Professor,
significantly contribute to the assessment of voice disorders as well as provide early detection of voice pathologies. These systems used machine learning techniques, regarded as highly promising in the detection of vocal pathologies. The goal of this research is to create a powerful feature extraction voice pathology detection tool based on Deep Learning.
These algorithms' primary function is to evaluate and recognise the voice disorder, and then to build a system capable of dealing with sound waves pathologies in order to differentiate pathological voices from healthy voices.
This paper describes a system for detecting voice pathology that uses machine learning to categorize voice signals as healthy or pathological. Various features such as MelFrequency Cepstral Coefficients (MFCC), Linear Predictive Analysis (LPC), Wavelet Packet Decomposition (WPD), Cepstral Analysis (CA), Jitter, Shimmer, Pulse, Pitch, Harmonicity, Intensity, Energy, and Entropy are extracted during this phase. Three widely used measurements are used to evaluate the proposed method: accuracy, sensitivity, and specificity.
The primary goal of our proposed method is to develop voice pathology detection systems that can effectively contribute to the assessment of voice disorders and provide early detection of voice pathologies.
Key Words: Voice pathology, MFCC, CNN, LPC, SVM
1. INTRODUCTION Machine learning skills are extremely valuable in classifying at least two classes, especially in speech processing. Furthermore, these methods have been employed in a range of healthcare environments. Voice pathology detection is one of these medical applications. If a person has difficulty or hoarseness in their speech as a result of a speech organ defect, psychological disorder, accident, autism, or other conditions, they are considered to have a vocal pathology problem. The existence of voice disorders or abnormalities in the vocal tract interferes with the glottis' normal vibrating sequence, resulted in hoarseness. For detecting vocal pathology, traditional medical diagnostic procedures are useless. These techniques are mainly based on vocal cord examination, which can lead to confusion and incorrect assesments. Furthermore; these methods necessitate a wide range of equipment, take more time, and are inefficient in terms of cost. One of the most difficult research areas in speech analysis and processing is the mechanisms of pathological voice detection and classification. Voice disorder detection techniques have advanced significantly thanks to the use of machine learning algorithms, which have demonstrated their efficiency and productivity throughout diagnostic applications in recent © 2022, IRJET
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Impact Factor value: 7.529
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2. OBJECTIVE
3. PROBLEM STATEMENT Suggested research has demonstrated that anomaly based detection techniques could contribute effectively towards the evaluation of voice disorders and provide earlier diagnosis of voice Pathologies. These systems used machine learning techniques that are thought to be quite promising for detecting voice disorders. However, the majority of proposed systems for detecting voice disorders made use of a limited database. Furthermore, one of the most difficult issues for these techniques is their low accuracy rate. Several difficulties in speech synthesis computation have now been discussed, which include comprehension evaluation, microphone validation, and identification of para linguistic events in voice, including such emotional responses and pathologies.
4. LITERATURE SURVEY The authors of [1] did work on vocal signal processing techniques in order to detect voice disorders. The extracted voice samples have been focused on glottal signal parameters, and SVM and K-Nearest Neighbor Boring classifiers were used (k-NN).The SVD database is used for voice samples in this method, with 71 pathological and 34 healthy voice samples collected, respectively. SVM was 98.5 percent accurate, while K-NN was 88.2 percent accuracy, according to the results. However, both the healthy and pathological voice samples are considered small. In [2] explored and investigated the symptom severity disease voice using CNN for classification and Fourier-based synchro squeezing transform (FSST) for feature extraction. ISO 9001:2008 Certified Journal
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