Speaker recognition method based on Mel frequency cepstrum coefficient and inverted Mel frequency cepstrum coefficient

Hu, Fengsong; Zhang, Xuan

doi:10.3724/sp.j.1087.2012.02542

Cited by 2 publications

(1 citation statement)

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“…At the same time, the voice is divided into speaker space and environment space to solve the channel caused by the environment. Using PLDA to realize channel compensation will further enhance the reliability of the extracted I-vector [4].…”

Section: Speech Recognition Training Template Selectionmentioning

confidence: 99%

Towards Speech Recognition and Training Utilization in the Nuclear Power Main Control Room

Wang

Deng

Duan

et al. 2022

J. Phys.: Conf. Ser.

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Speech, as the material shell of language, is the external form of language, and is the symbol system that most directly records people’s thinking activities. As a means of interaction, it has the characteristics of simplicity and speed. For a long time, due to insufficient recognition accuracy, it cannot be applied in industry. However, with the rapid development of big data and machine learning technology, the training effect of speech model has greatly increased, and the reliability of speech recognition has been greatly improved. The application of speech interaction in nuclear power plants with extremely high safety requirements brings feasibility. The purpose of this system is to establish an intelligent speech control system and its related speech model training system, and use Kaldi and Python scripts as model training scripts to train the corpus. And designed experiments to verify that after using speech recognition, it can effectively reduce the operation time by about 45%, and the overall task execution efficiency has been significantly improved. Therefore, the use of speech recognition can significantly reduce the operator’s task load and reduce the probability of human error.

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Section: Speech Recognition Training Template Selectionmentioning

confidence: 99%

Towards Speech Recognition and Training Utilization in the Nuclear Power Main Control Room

Wang

Deng

Duan

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

Research of speaker recognition based on the weighted fisher ratio of MFCC

Huang

Gong

et al. 2013

Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

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Feature extraction and pattern recognition are two important technologies of speaker recognition system. We introduced the existing speaker recognition technology in this paper, proposed and implemented a speaker recognition algorithm based on the weighted fisher ratio of MFCC. Compared with the traditional feature selection methods, the characteristic vector obtained via this algorithm has the greatest degree of differentiation in the same dimension. To evaluate performance of this algorithm, we built a small speaker recognition system based on the MATLAB. According to the test results, the speaker recognition algorithm we proposed in this paper, can significantly increase the accuracy rate of training and recognition, and reduce the data required by calculation, in the case of keeping a higher recognition rate.

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Speaker recognition method based on Mel frequency cepstrum coefficient and inverted Mel frequency cepstrum coefficient

Cited by 2 publications

References 0 publications

Towards Speech Recognition and Training Utilization in the Nuclear Power Main Control Room

Towards Speech Recognition and Training Utilization in the Nuclear Power Main Control Room

Research of speaker recognition based on the weighted fisher ratio of MFCC

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