Speech Feature Extraction at Different Mode with Application to Shouted Speech Recognition System used for Women Safety

2017 ◽  
Vol V6 (08) ◽  
Author(s):  
Anly Paul ◽  
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Speech Feature ◽  
Speech Feature Extraction

A Speech Feature Extraction Algorithm Based on the Nonlinear Hopf Cochlea Model

2013 ◽  
Vol 416-417 ◽  
pp. 1176-1180
Author(s):  
Jian Guo Xing ◽  
Min Xu ◽  
Ji Xiang Zhu
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Filter Banks ◽  
Recognition Rate ◽  
Recognition System ◽  
Center Frequency ◽  
Characteristic Parameters ◽  
Discrete Cosine Transformation ◽  
Speech Feature ◽  
Speech Feature Extraction

The performance of speech recognition system is well or not is closely related to the characteristic parameters. For emulating human auditory system, a new method of speech feature extraction based on Hopf filter banks is presented. We modeled the extraction process of the MFCC, and used Hopf filter banks instead of the triangular filter banks. Then, we according the characteristics of Basilar Membranes in the cochlea to adjust the center frequency and bandwidth of the filter. The test speech goes through the Hopf filter banks, multi-dimensional eigenvectors will be obtained. After that, by Discrete Cosine Transformation, we will get the Hopf cepstral coefficients of the speech. Comparing with traditional feature MFCC, the speech recognition systems with Hopf characteristic parameters have better recognition rate and robustness characteristics in low Signal Noise Ratio (SNR) environment.

scholarly journals A Robust Isolated Automatic Speech Recognition System using Machine Learning Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 2325-2331
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Speaker Recognition ◽  
Search Algorithm ◽  
Recognition System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Speech Recognition System ◽  
Work Done

In order to make fast communication between human and machine, speech recognition system are used. Number of speech recognition systems have been developed by various researchers. For example speech recognition, speaker verification and speaker recognition. The basic stages of speech recognition system are pre-processing, feature extraction and feature selection and classification. Numerous works have been done for improvement of all these stages to get accurate and better results. In this paper the main focus is given to addition of machine learning in speech recognition system. This paper covers architecture of ASR that helps in getting idea about basic stages of speech recognition system. Then focus is given to the use of machine learning in ASR. The work done by various researchers using Support vector machine and artificial neural network is also covered in a section of the paper. Along with this review is presented on work done using SVM, ELM, ANN, Naive Bayes and kNN classifier. The simulation results show that the best accuracy is achieved using ELM classifier. The last section of paper covers the results obtained by using proposed approaches in which SVM, ANN with Cuckoo search algorithm and ANN with back propagation classifier is used. The focus is also on the improvement of pre-processing and feature extraction processes.

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