Abstract
The signal corresponding to English speech contains a lot of redundant information and environmental interference information, which will produce a lot of distortion in the process of English speech translation signal recognition. Based on this, a large number of studies focus on encoding and processing English speech, so as to achieve high-precision speech recognition. The traditional wavelet denoising algorithm plays an obvious role in the recognition of English speech translation signals, which mainly depends on the excellent local time-frequency domain characteristics of the wavelet signal algorithm, but the traditional wavelet signal algorithm is still difficult to select the recognition threshold, and the recognition accuracy is easy to be affected. Based on this, this paper will improve the traditional wavelet denoising algorithm, abandon the single-threshold judgment of the original traditional algorithm, innovatively adopt the combination of soft threshold and hard threshold, further solve the distortion problem of the denoising algorithm in the process of English speech translation signal recognition, improve the signal-to-noise ratio of English speech recognition, and further reduce the root mean square error of the signal. Good noise reduction effect is realized, and the accuracy of speech recognition is improved. In the experiment, the algorithm is compared with the traditional algorithm based on MATLAB simulation software. The simulation results are consistent with the actual theoretical results. At the same time, the algorithm proposed in this paper has obvious advantages in the recognition accuracy of English speech translation signals, which reflects the superiority and practical value of the algorithm.
Highlights
Modern information technology, multimedia technology and artificial intelligence technology are combined with speech recognition technology, so as to realize the extraction, processing, and analysis of all kinds of speech
In the conventional underlying algorithms of English speech recognition, the main basic recognition principles are mainly focused on the recognition algorithm based on phonetics, the recognition algorithm based on speech template matching, and the speech recognition algorithm based on neural network
In view of the problems that the above traditional wavelet signal algorithm still has difficulties in selecting the recognition threshold and the recognition accuracy is easy to be affected, this paper will improve the wavelet denoising algorithm and analyze the combination of soft threshold and hard threshold according to its corresponding algorithm decomposition ability and denoising threshold
Summary
Multimedia technology and artificial intelligence technology are combined with speech recognition technology, so as to realize the extraction, processing, and analysis of all kinds of speech. In view of the problems that the above traditional wavelet signal algorithm still has difficulties in selecting the recognition threshold and the recognition accuracy is easy to be affected, this paper will improve the wavelet denoising algorithm and analyze the combination of soft threshold and hard threshold according to its corresponding algorithm decomposition ability and denoising threshold It further solves the distortion problem in the process of English speech translation signal recognition of denoising algorithm, improves the signal-to-noise ratio of English speech recognition, and further reduces the root mean square error of signal, so as to achieve good noise reduction effect and improve the accuracy of speech recognition.
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