Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional meth...Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification.In this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations.Prior classification of speech signals,the study extracts the essential features from the speech signal using Cepstral Analysis.The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate.Hence to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient.The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets.The validation of testing sets is evaluated using RL that provides feedback to Classifiers.Finally,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input query.The results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.展开更多
A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net -work. Speech signals were analyzed sequentially and a feature vector was obtaine...A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net -work. Speech signals were analyzed sequentially and a feature vector was obtained for each segment . The feature vector served as input to a 3-layer feedforward network in which voiced, unvoiced, and silence classification was made. The network had a 6-12-3 node architecture and was trained using the generalized delta rule for back propagation of error . The performance of the network was evaluated using speech samples from 3 male and 3 female speakers . A speaker-dependent classification rate of 94.7% and speaker-independent classification rate of 94.3% were obtained. It is concluded that the voiced, unvoiced , and silence classification of speech can be effectively accomplished using a multilayer feedforward network.展开更多
文摘Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification.In this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations.Prior classification of speech signals,the study extracts the essential features from the speech signal using Cepstral Analysis.The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate.Hence to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient.The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets.The validation of testing sets is evaluated using RL that provides feedback to Classifiers.Finally,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input query.The results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.
文摘A procedure has been developed for making voiced, unvoiced, and silence classifications of speech by using a multilayer feedforward net -work. Speech signals were analyzed sequentially and a feature vector was obtained for each segment . The feature vector served as input to a 3-layer feedforward network in which voiced, unvoiced, and silence classification was made. The network had a 6-12-3 node architecture and was trained using the generalized delta rule for back propagation of error . The performance of the network was evaluated using speech samples from 3 male and 3 female speakers . A speaker-dependent classification rate of 94.7% and speaker-independent classification rate of 94.3% were obtained. It is concluded that the voiced, unvoiced , and silence classification of speech can be effectively accomplished using a multilayer feedforward network.
