Voice recognition through the use of Gabor transform and heuristic algorithm

Woźniak, Marcin; Połap, Dawid

doi:10.1515/eletel-2017-0021

Cited by 16 publications

(13 citation statements)

References 10 publications

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“…Although the research has good classification accuracy, it still cannot meet the demand in terms of time complexity. Literature [6] regards the perceptual linear prediction cepstrum coefficient as one of the short-term features. It uses different classifiers for short-term features and long-term features and finally gets better classification efficiency.…”

Section: Related Workmentioning

confidence: 99%

The Improving Effect of Intelligent Speech Recognition System on English Learning

Luo¹

2022

Advances in Multimedia

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To improve the effect of English learning in the context of smart education, this study combines speech coding to improve the intelligent speech recognition algorithm, builds an intelligent English learning system, combines the characteristics of human ears, and studies a coding strategy of a psychoacoustic masking model based on the characteristics of human ears. Moreover, this study analyzes in detail the basic principles and implementation process of the psychoacoustic model coding strategy based on the characteristics of the human ear and completes the channel selection by calculating the masking threshold. In addition, this study verifies the effectiveness of the algorithm in this study through simulation experiments. Finally, this study builds a smart speech recognition system based on this model and uses simulation experiments to verify the effect of smart speech recognition on English learning. To improve the voice recognition effect of smart speech, band-pass filtering and envelope detection adopt the gammatone filter bank and Meddis inner hair cell model in the mathematical model of the cochlear system; at the same time, the masking effect model of psychoacoustics is introduced in the channel selection stage to prevent noise. Sex has been improved, and the recognition effect of smart voice has been improved. The analysis shows that the intelligent speech recognition system proposed in this study can effectively improve the effect of English learning. In particular, it has a great effect on improving the effect of oral learning.

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Section: Related Workmentioning

confidence: 99%

The Improving Effect of Intelligent Speech Recognition System on English Learning

Luo¹

2022

Advances in Multimedia

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“…e literature proposed a method combining the n-gram language model with the naive Bayes classifier to achieve the purpose of classifying language types [9]. e literature proposed a method to realize language recognition based on the user's previous messages and the related content embedded in the messages for Twitter information [10].…”

Section: Related Workmentioning

confidence: 99%

Simulation of English Speech Recognition Based on Improved Extreme Random Forest Classification

Hao

2022

Computational Intelligence and Neuroscience

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Existing speech recognition systems are only for mainstream audio types; there is little research on language types; the system is subject to relatively large restrictions; and the recognition rate is not high. Therefore, how to use an efficient classifier to make a speech recognition system with a high recognition rate is one of the current research focuses. Based on the idea of machine learning, this study combines the computational random forest classification method to improve the algorithm and builds an English speech recognition model based on machine learning. Moreover, this study uses a lightweight model and its improved model to recognize speech signals and directly performs adaptive wavelet threshold shrinkage and denoising on the generated time-frequency images. In addition, this study uses the EI strong classifier to replace the softmax of the lightweight AlexNet model, which further improves the recognition accuracy under a low signal-to-noise ratio. Finally, this study designs experiments to verify the model effect. The research results show that the effect of the model constructed in this study is good.

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“…The literature [4] proved that when the subject remains awake for 24 hours, the duration of the pause in the speech gradually increases, and the change of the fourth formant frequency of the vowel pronunciation decreases. The literature [5] used the non-linear dynamic characteristics of speech to detect speech fatigue. The literature [6] analyzed the relationship between relevant features and fatigue in speech recognition.…”

Section: Related Workmentioning

confidence: 99%

Research on intelligent language translation system based on deep learning algorithm

Shi

2021

Soft Comput

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In order to improve the effect of intelligent language translation, this paper analyzes the problems of the MSE cost function used by most of the current DNN-based speech enhancement algorithms, and proposes a deep learning speech enhancement algorithm based on perceptionrelated cost functions. Moreover, this paper embeds the suppression gain parameter estimation into the architecture of the traditional speech enhancement algorithm, and converts the relationship between the noisy speech spectrum and the enhanced speech spectrum into a simple multiplication relationship based on suppression gain combined with deep learning algorithms to construct an intelligent language translation system. Moreover, this paper evaluates the translation effect of the system, analyzes the actual results, and uses simulation tests to verify the performance of the intelligent language translation model constructed in this paper. From the experimental results, it can be seen that the intelligent language translation system based on deep learning algorithms has good results.

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Voice recognition through the use of Gabor transform and heuristic algorithm

Cited by 16 publications

References 10 publications

The Improving Effect of Intelligent Speech Recognition System on English Learning

The Improving Effect of Intelligent Speech Recognition System on English Learning

Simulation of English Speech Recognition Based on Improved Extreme Random Forest Classification

Research on intelligent language translation system based on deep learning algorithm

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