Text Dependent Speaker Identification System Research Articles

Text dependent speaker identification system based on deep learning

Speaker identification techniques are one of those most advanced modern technologies and there are many different systems had been developed, from methods that used to extract characteristics and classification. The applications of Speech identification are quite difficult and requires modern technologies with a large number of audio samples and resources. In this research, the system of speaker identification had been designed based on a text (the word or sentences are pre-defined) which give the system the capability to identify the speaker in the least time, number of training samples and resources. The system consists four main parts, the first one is to create audio databases. In the study, two audio databases were relied upon, the first being a database (QS- Dataset) and the second database (audioMNIST_meta). The databases were processed and configured in a way that was explained in the body of the research later. The second part of the research is to extract the characteristics through the pitch coefficients algorithm, while the third part is the use of the neural network as a classifier. And the last part of the research is to verify the work and results of the system. The test results showed the ability of the MNN network to deal with the smallest number of data, as it achieved a percentage of 100%. As for large data, it ranged from 80% to 81%. Unlike CNN network, the results were not good for the few data, from 60% to 76%, and with large data it was The results are excellent, from 91% to 96%.

Speaker identification based on combination of MFCC and UMRT based features

This paper introduces an isolated word speaker identification system based on a new feature extractor and using Artificial Neural Network. The system is designed for both text independent and text dependent speaker identification system for English words. The speech is recorded using audio wave recorder. Then the preprocessing is applied for the given speech signals. UMRT is a transform which has been used for image compression. Combinations of MFCC and UMRT are taken and are used as a feature extractor. The classification of the features is done using Multi-layer perceptron with back propagation algorithm. The accuracy is taken using confusion matrix. The accuracy achieved is around 97.91% for speech dependent systems while for speech independent system the accuracy is around 94.44%.

Speaker Adaptation on Hidden Markov Model using MFCC and Rasta-PLP and Comparative Study

This work compares the performance of the Mel-Frequency Cepstral Coefficient (MFCC) and Perceptual Linear Prediction (PLP) features for developing a text-dependent speaker identification system. Continuously spoken Hindi speech sentences have been used to train the HMM models using HTK toolkit for each speaker separately. The experiments have been performed using a set of 200 continuously spoken sentences with vocabulary of 20000 isolated words using a database of 100 speakers. The results show an accuracy of 92.26% recognition when PLP features have been used and accuracy of 91.18% for MFCC features. A confusion matrix has been created for all the 20 test speakers based on the recognition scores obtained for each of these speakers and their confusion with other speakers. Performance has been compared in the closed set and open set conditions of testing and as it is expected, the performance in the closed set condition is far better than the open set. We propose that if PLP features are used in place of MFCC, they may provide improvement in speaker identification accuracy by reducing the cases of false acceptance.

Text Dependent Speaker Identification using Hidden Markchov Model and Mel Frequency Cepstrum Coefficient

Speaker identification is a biometric process. The objective of speaker identification is to extract, characterize and recognize the information about speaker identity. Speaker Recognition technology has recently been used in a vast number of commercial areas successfully such as in voice based biometrics; voice controlled appliances, security control for confidential information, remote access to computers and many more interesting areas. A speaker identification system has two phases which are the training phase and the testing phase. Feature extraction is the first step for each phase in speaker recognition. Many algorithms are used for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. In the identification phase, the existing reference templates are compared with the unknown voice input. In this thesis, Hidden Markov Model (HMM) method is used as the training/recognition algorithm which makes the final decision about the specification of the speaker by comparing unknown features to all models in the database and selecting the best matching model. i, e. the highest scored model. The speaker who obtains the highest score is selected as the target speaker.

Text Dependent Speaker Identification System using Discrete HMM in Noise

In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Hidden Markov Model technique with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, ΔMFCC, ΔΔMFCC, LPC and LPCC have been used to extract the features. After parameterization of the speech, Discrete Hidden Markov Model has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. The performance of this identification is almost different in each case. The highest speaker identification rate of 93[%] for noiseless environment and 69.27[%] for noisy environment have been achieved in the close set text dependent speaker identification system. General Terms Speaker Identification, Speech Feature Extraction.

Enhancing speaker identification performance under the shouted talking condition using second-order circular hidden Markov models

It is known that the performance of speaker identification systems is high under the neutral talking condition; however, the performance deteriorates under the shouted talking condition. In this paper, second-order circular hidden Markov models (CHMM2s) have been proposed and implemented to enhance the performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that CHMM2s significantly improve speaker identification performance under such a condition compared to the first-order left-to-right hidden Markov models (LTRHMM1s), second-order left-to-right hidden Markov models (LTRHMM2s), and the first-order circular hidden Markov models (CHMM1s). Under the shouted talking condition, our results show that the average speaker identification performance is 23% based on LTRHMM1s, 59% based on LTRHMM2s, and 60% based on CHMM1s. On the other hand, the average speaker identification performance under the same talking condition based on CHMM2s is 72%.

Improving Speaker Identification Performance Under the Shouted Talking Condition Using the Second-Order Hidden Markov Models

Speaker identification systems perform well under the neutral talking condition; however, they suffer sharp degradation under the shouted talking condition. In this paper, the second-order hidden Markov models (HMM2s) have been used to improve the recognition performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that HMM2s significantly improve the speaker identification performance compared to the first-order hidden Markov models (HMM1s). The average speaker identification performance under the shouted talking condition based on HMM1s is. On the other hand, the average speaker identification performance based on HMM2s is.

Improvement Of The Text Dependent Speaker Identification System Using Discrete MMM With Cepstral Based Features

In this paper, an improved strategy for automated text based speaker identification scheme has been proposed. The identification process incorporates the Hidden Markov Model technique. After preprocessing the speech, HMM is used in the learning and identification. Features are extracted by different techniques such as RCC, MFCC, ΔMFCC, ΔΔMFCC, LPC and LPCC which is almost different in each case. The highest identification rate of 93% has been achieved in the close set text dependent speaker identification system. Keywords: Biometric Technologies; Automatic Speaker Identification; Cepstral Coefficients; Feature Extraction; Hidden Markov Model. DOI: http://dx.doi.org/10.3329/diujst.v6i2.9341 DIUJST 2011; 6(2): 14-21