Speaker identification analysis for SGMM with k-means and fuzzy C-means clustering using SVM statistical technique

2021 ◽  
Vol 25 (3) ◽  
pp. 309-314
Author(s):  
K. Manikandan ◽  
E. Chandra
Keyword(s):  
Speaker Identification ◽  
Gaussian Mixture ◽  
Statistical Technique ◽  
Identification System ◽  
Mel Frequency Cepstral Coefficients ◽  
Fuzzy C Means ◽  
Memory Footprint ◽  
Fuzzy C Means Clustering ◽  
Input Model ◽  
Identification Analysis

Speaker Identification denotes the speech samples of known speaker and it identifies the best matches of the input model. The SGMFC method is the combination of Sub Gaussian Mixture Model (SGMM) with the Mel-frequency Cepstral Coefficients (MFCC) for feature extraction. The SGMFC method minimizes the error rate, memory footprint and also computational throughput measure needs of a medium-vocabulary speaker identification system, supposed for preparation on a transportable or otherwise. Fuzzy C-means and k-means clustering are used in the SGMM method to attain the improved efficiency and their outcomes with parameters such as precision, sensitivity and specificity are compared.

scholarly journals Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

2020 ◽  
Vol 18 (2) ◽  
pp. 782
Author(s):  
Musab T. S. Al-Kaltakchi ◽  
Haithem Abd Al-Raheem Taha ◽  
Mohanad Abd Shehab ◽  
Mohamed A.M. Abdullah
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Gaussian Mixture ◽  
Identification Accuracy ◽  
Identification System ◽  
Good Representation ◽  
Mel Frequency Cepstral Coefficients ◽  
Normalization Methods ◽  
Cepstral Coefficients

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

scholarly journals FPGA Implementation for GMM-Based Speaker Identification

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Phaklen EhKan ◽  
Timothy Allen ◽  
Steven F. Quigley
Keyword(s):  
Vocal Tract ◽  
Speaker Identification ◽  
Hardware Acceleration ◽  
Statistical Modelling ◽  
Gaussian Mixture ◽  
Personal Identification ◽  
Identification System ◽  
Call Centre ◽  
Mel Frequency Cepstral Coefficients ◽  
High Level

In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors such as Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal. A well-known statistical modelling process, the Gaussian Mixture Model (GMM), then models the distribution of each speaker's MFCCs in a multidimensional acoustic space. The GMM-based speaker identification system has features that make it promising for hardware acceleration. This paper describes the hardware implementation for classification of a text-independent GMM-based speaker identification system. The aim was to produce a system that can perform simultaneous identification of large numbers of voice streams in real time. This has important potential applications in security and in automated call centre applications. A speedup factor of ninety was achieved compared to a software implementation on a standard PC.

scholarly journals Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

2021 ◽  
Vol 38 (6) ◽  
pp. 1793-1799
Author(s):  
Shivaprasad Satla ◽  
Sadanandam Manchala
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Processing ◽  
Deep Neural Network ◽  
Speaker Identification ◽  
Research Work ◽  
Gaussian Mixture ◽  
Vital Role ◽  
Identification System ◽  
Mel Frequency Cepstral Coefficients

Dialect Identification is the process of identifies the dialects of particular standard language. The Telugu Language is one of the historical and important languages. Like any other language Telugu also contains mainly three dialects Telangana, Costa Andhra and Rayalaseema. The research work in dialect identification is very less compare to Language identification because of dearth of database. In any dialects identification system, the database and feature engineering play vital roles because of most the words are similar in pronunciation and also most of the researchers apply statistical approaches like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), etc. to work on speech processing applications. But in today's world, neural networks play a vital role in all application domains and produce good results. One of the types of the neural networks is Deep Neural Networks (DNN) and it is used to achieve the state of the art performance in several fields such as speech recognition, speaker identification. In this, the Deep Neural Network (DNN) based model Multilayer Perceptron is used to identify the regional dialects of the Telugu Language using enhanced Mel Frequency Cepstral Coefficients (MFCC) features. To do this, created a database of the Telugu dialects with the duration of 5h and 45m collected from different speakers in different environments. The results produced by DNN model compared with HMM and GMM model and it is observed that the DNN model provides good performance.

Speaker Identification in Total Variability Space

2013 ◽  
Vol 401-403 ◽  
pp. 1489-1492
Author(s):  
Qiang Li ◽  
Ming Bing Zhao ◽  
Yong Feng
Keyword(s):  
Speaker Identification ◽  
Gaussian Mixture ◽  
Dimensional Subspace ◽  
Identification System ◽  
Accurate Identification ◽  
Identification Rate ◽  
Total Variability ◽  
Rate Improvement ◽  
Low Dimensional ◽  
The Given

Gaussian Mixture Model-Universal Background Model based approaches have been popular used for speaker identification task. But in real complex environment the identification system performs too much worse than in laboratory, and the main reason is the mismatch of the training and testing channel and also the variability of the speaker himself. In this paper we introduce i-vector to the speaker identification system. In i-vector approach, a low dimensional subspace called total variability space is used to estimate both speaker and channel variability. Baum-Welch statistics are first computed over the given UBM to estimate the total variability. From the experiment results, we obtain 2.44% relative accurate identification rate improvement when using total variability space to compensate the mismatch of the variabilities from both the speaker and channel.

An Experimental Comparison of Modeling Techniques and Combination of Speaker – Specific Information from Different Languages for Multilingual Speaker Identification

2016 ◽  
Vol 25 (4) ◽  
pp. 529-538
Author(s):  
H.S. Jayanna ◽  
B.G. Nagaraja
Keyword(s):  
Speaker Recognition ◽  
English Language ◽  
Speaker Identification ◽  
Poor Performance ◽  
Gaussian Mixture ◽  
Experimental Comparison ◽  
Identification System ◽  
Specific Information ◽  
Self Organizing Map ◽  
Modeling Techniques

AbstractMost of the state-of-the-art speaker identification systems work on a monolingual (preferably English) scenario. Therefore, English-language autocratic countries can use the system efficiently for speaker recognition. However, there are many countries, including India, that are multilingual in nature. People in such countries have habituated to speak multiple languages. The existing speaker identification system may yield poor performance if a speaker’s train and test data are in different languages. Thus, developing a robust multilingual speaker identification system is an issue in many countries. In this work, an experimental evaluation of the modeling techniques, including self-organizing map (SOM), learning vector quantization (LVQ), and Gaussian mixture model-universal background model (GMM-UBM) classifiers for multilingual speaker identification, is presented. The monolingual and crosslingual speaker identification studies are conducted using 50 speakers of our own database. It is observed from the experimental results that the GMM-UBM classifier gives better identification performance than the SOM and LVQ classifiers. Furthermore, we propose a combination of speaker-specific information from different languages for crosslingual speaker identification, and it is observed that the combination feature gives better performance in all the crosslingual speaker identification experiments.

scholarly journals Arabic Word Dependent Speaker Identification System Using Artificial Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Security System ◽  
Identification System ◽  
Accuracy Rate ◽  
Mel Frequency Cepstral Coefficients ◽  
Arabic Speakers ◽  
Arabic Word ◽  
Processing Steps

The security of systems is a vital issue for any society. Hence, the need for authentication mechanisms that protect the confidentiality of users is important. This paper proposes a speech based security system that is able to identify Arabic speakers by using an Arabic word )شكرا (which means “Thank you”. The pre-processing steps are performed on the speech signals to enhance the signal to noise ratio. Features of speakers are obtained as Mel-Frequency Cepstral Coefficients (MFCC). Moreover, feature selection (FS) and radial basis function neural network (RBFNN) are implemented to classify and identify speakers. The proposed security system gives a 97.5% accuracy rate in its user identification process.

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