Artificial Neural Network Based Pathological Voice Classification Using Mfcc Features
نویسندگان
چکیده
The analysis of pathological voice is a challenging and an important area of research in speech processing. Acoustic voice analysis can be used to characterize the pathological voices with the aid of the speech signals recorded from the patients. This paper presents a method for the identification and classification of pathological voice using Artificial Neural Network. Multilayer Perceptron Neural Network (MLPNN), Generalised Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) are used for classifying the pathological voices. Mel-Frequency Cepstral Coefficients (MFCC) features extracted from audio recordings are used for this purpose.
منابع مشابه
Singer Traits Identification using Deep Neural Network
The author investigates automatic recognition of singers’ gender and age through audio features using deep neural network (DNN). Features of each singing voice, fundamental frequency and Mel-Frequency Cepstrum Coefficients (MFCC) are extracted for neural network training. 10,000 singing voice from Smule’s Sing! Karaoke app is used for training and evaluation, and the DNN-based method achieves a...
متن کاملArtificial Neural Networks and Support Vector Machine for Voice Disorders Identification
The diagnosis of voice diseases through the invasive medical techniques is an efficient way but it is often uncomfortable for patients, therefore, the automatic speech recognition methods have attracted more and more interest recent years and have known a real success in the identification of voice impairments. In this context, this paper proposes a reliable algorithm for voice disorders identi...
متن کاملSpeaker recognition using pattern recognition neural network and feedforward neural network
Neha Chauhan Birla Institute of Technology, Mesra, Ranchi Abstract— Speaker Recognition is the computing task of validating a user’s claimed identity using speech characteristics. Main objective of speech recognition system is to communication with a device through our voice. Mel frequency Cepstral Coefficient (MFCC) features are combined with pitch and root mean square values and tested for im...
متن کاملClassification of Normal and Pathological Voice Using SVM and RBFNN
The identification and classification of pathological voice are still a challenging area of research in speech processing. Acoustic features of speech are used mainly to discriminate normal voices from pathological voices. This paper explores and compares various classification models to find the ability of acoustic parameters in differentiating normal voices from pathological voices. An attemp...
متن کاملIdentification of Houseplants Using Neuro-vision Based Multi-stage Classification System
In this paper, we present a machine vision system that was developed on the basis of neural networks to identify twelve houseplants. Image processing system was used to extract 41 features of color, texture and shape from the images taken from front and back of the leaves. The features were fed into the neural network system as the recognition criteria and inputs. Multilayer perceptron (MLP) ne...
متن کامل