Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Automatic Speaker Recognition System based on Optimised Machine Learning Algorithms

2019 ◽  
Author(s):  
Tumisho Billson Mokgonyane ◽  
Tshephisho Joseph Sefara ◽  
Thipe Isaiah Modipa ◽  
Madimetja Jonas Manamela
Keyword(s):  
Machine Learning ◽  
Speaker Recognition ◽  
Learning Algorithms ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Automatic Speaker Recognition

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.

Summary and initial results of the 2013-2014 speaker recognition i-vector machine learning challenge

2014 ◽  
Author(s):  
Désiré Bansé ◽  
George R. Doddington ◽  
Daniel Garcia-Romero ◽  
John J. Godfrey ◽  
Craig S. Greenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Speaker Recognition ◽  
Initial Results

An efficient speaker recognition using quantum neural network

2018 ◽  
Vol 32 (31) ◽  
pp. 1850384 ◽  
Author(s):  
Rupinderdeep Kaur ◽  
R. K. Sharma ◽  
Parteek Kumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Speaker Recognition ◽  
Bias Reduction ◽  
Speech Signals ◽  
Statistical Features ◽  
Quantum Neural Network ◽  
Inductive Bias

Speaker recognition is the technique to identify the identity of a person from statistical features obtained from speech signals. Many speaker recognition techniques have been designed and implemented so far to efficiently recognize the speaker. From the existing review, it is found that the existing speaker recognition techniques suffer from the over-fitting issues. Therefore, to overcome the over-fitting issue in this paper, we design, a novel ensemble-based quantum neural network. It selects one base model (i.e. expert) for each query, and concentrates on inductive bias reduction. A set of quantum neural networks are trained by considering different kinds of quantum features and are afterwards used to recognize the speaker. In the end, ensembling is used to combine these classification results. Extensive experiments have been carried out by considering the proposed technique and existing competitive machine learning-based speaker recognition techniques on speaker recognition data. It is observed that the proposed technique outperforms existing speaker recognition techniques in terms of accuracy and sensitivity by 1.371% and 1.291%, respectively.

Efficient Approach of Automatic Speech Emotion Recognition (ASR) Using Mutual Information

2021 ◽  
Vol 9 (1) ◽  
pp. 595-603
Author(s):  
Shivangi Srivastav, Rajiv Ranjan Tewari
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Mutual Information ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Learning Algorithms ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Speech Emotion Recognition

Speech is a significant quality for distinguishing a person in daily human to human interaction/ communication. Like other biometric measures, such as face, iris and fingerprints, voice can therefore be used as a biometric measure for perceiving or identifying the person. Speaker recognition is almost the same as a kind of voice recognition in which the speaker is identified from the expression instead of the message. Automatic Speaker Recognition (ASR) is the way to identify people who rely on highlights that are omitted from speech expressions. Speech signals are awesome correspondence media that constantly pass on rich and useful knowledge, such as a speaker's feeling, sexual orientation, complement, and other interesting attributes. In any speaker identification, the essential task is to delete helpful highlights and allow for significant examples of speaker models. Hypothetical description, organization of the full state of feeling and the modalities of articulation of feeling are added. A SER framework is developed to conduct this investigation, in view of different classifiers and different techniques for extracting highlights. In this work various machine learning algorithms are investigated to identify decision boundary in feature space of audio signals. Moreover novelty of this art lies in improving the performance of classical machine learning algorithms using information theory based feature selection methods. The higher accuracy retrieved is 96 percent using Random forest algorithm incorporated with Joint Mutual information feature selection method.

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