Implementation of noise reduction optimization process of language lab recording based on RNN model

Yuzhu Zhang, Wei Zeng, Qiang Liao


Speech signal noise reduction involves speech signal processing technology, which is one of the core technologies in the
field of information application. Speech signal optimization analysis and processing is also a popular direction in the field of artificial
intelligence. In this paper, a speech signal optimization processing method based on the recurrent neural network RNN model is proposed for
the language laboratory recording signal as the research object. The key technologies in the fi eld of deep learning are introduced for audio
noise reduction processing. By using experimental simulation, the signal-to-noise ratio of the recording signal is improved, so as to obtain a
more ideal language laboratory recording audio fi le.


Speech signal; Deep learning; Recurrent neural network RNN ; S ignal-to-noise ratio

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