Speech Perception Improvement Algorithm Based on a Dual-Path Long Short-Term Memory Network

Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address...

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Bibliographic Details
Published in:Bioengineering (Basel) 2023-11, Vol.10 (11), p.1325
Main Authors: Koh, Hyeong Il, Na, Sungdae, Kim, Myoung Nam
Format: Article
Language:English
Subjects:
Algorithms
Bioengineering
Datasets
Deep learning
dual-path network
encoder–decoder structure
Fourier transforms
Frequency domain analysis
Intelligibility
Long short-term memory
LSTM
Neural networks
Semantics
Signal processing
spectral extension block
Speech
speech enhancement
Speech perception
Speech processing
STFT
Time-frequency analysis
Waveforms
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Summary:Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, this paper introduces a speech enhancement model designed with a dual-path structure that identifies key speech characteristics in both the time and time–frequency domains. Specifically, the time path aims to model semantic features hidden in the waveform, while the time–frequency path attempts to compensate for the spectral details via a spectral extension block. These two paths enhance temporal and spectral features via mask functions modeled as LSTM, respectively, offering a comprehensive approach to speech enhancement. Experimental results show that the proposed dual-path LSTM network consistently outperforms conventional single-domain speech enhancement methods in terms of speech quality and intelligibility.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering10111325