LSTM Speech Enhancement Parameter Extraction
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Solution Overview
Problem
Existing speech enhancement technologies require high storage capacity to store noise reduction models for different speakers, which is inefficient and costly.
Innovation Solution
A method using a long short-term memory (LSTM) model to perform speech enhancement by obtaining and updating speech enhancement parameters for individual speakers, reducing the need for multiple models by storing only parameters and continuously training on new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If noise reduction models are stored for different speakers, then speech enhancement quality is improved, but storage capacity requirements increase
Solution Approach 1:
The patent extracts only the essential speech enhancement parameters from complete noise reduction models. Instead of storing entire models for each speaker, it identifies and stores only the critical parameters that capture individual speech characteristics, significantly reducing storage requirements while maintaining enhancement quality.
Solution Approach 2:
The patent transforms the representation of speaker-specific noise reduction capabilities from complete models to condensed parameter sets. By changing the data structure from full models to essential parameters, it achieves both quality preservation and storage reduction.
2Manufacturing precision
If adaptive training is performed on entire noise reduction models, then speaker-specific enhancement is improved, but training time and computational resources increase
Solution Approach 1:
The patent extracts only the essential parameters that need to be trained for each speaker, rather than performing adaptive training on entire noise reduction models. This selective parameter training dramatically reduces computational resources and training time while maintaining speaker-specific enhancement quality.
Solution Approach 2:
The patent applies partial training by focusing only on the essential parameters that capture speaker characteristics, rather than training the complete model. This partial action approach achieves sufficient speaker adaptation with significantly reduced computational overhead.
Data Source
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AI summary
A speech data processing method and apparatus, an electronic device and a computer-readable storage medium. The method comprises: receiving first speech data sent by a sending party, and acquiring corresponding speech enhancement parameters (S101); performing, on the basis of the acquired speech enhancement parameters, speech enhancement processing on the first speech data to obtain first speech enhancement data, and determining first speech enhancement parameters on the basis of the first speech enhancement data (S102); and sending the first speech enhancement data to a receiving party, and updating the acquired speech enhancement parameters with the first speech enhancement parameters so as to obtain updated speech enhancement parameters for use in performing, when second speech data sent by the sending party is received, speech enhancement processing on the second speech data on the basis of the updated speech enhancement parameters (S103).