Modified Diffusion Model for Speech Restoration
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Solution Overview
Problem
Existing speech enhancement methods fail to effectively restore degraded speech quality due to non-linear and lossy compression processes, leading to reduced intelligibility and suitability for applications like automatic speech recognition and speaker identification.
Innovation Solution
A modified diffusion model incorporating a deep CNN upsampler is trained to invert lossy transformations and restore missing information in speech signals, using a diffusion-based vocoder to generate estimated original speech from degraded mel-spectrum samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If speech compression algorithms are used to reduce sampling rate and compress input, then file size and transmission efficiency are improved, but speech quality and intelligibility deteriorate
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods (compression algorithms, filtering) with a neural network-based diffusion model. The model learns to reconstruct high-quality speech waveforms from compressed representations by modeling the probability distribution of speech signals, thereby avoiding the quality loss inherent in deterministic compression methods.
Solution Approach 2:
The patent changes the fundamental parameters of speech representation by using mel-spectrogram features as input to the diffusion model rather than raw waveforms. This parameter transformation allows the model to capture essential speech characteristics in a compressed form while reconstructing high-fidelity waveforms through the diffusion process.
2Quantity of substance
If clipping of speech is applied to compress signal, then data transmission is improved, but high frequency content is introduced with negative impact on quality
Solution Approach 1:
The patent introduces a neural network-based diffusion model as an intermediary between the compressed speech representation and the reconstructed waveform. This intermediary learns to smoothly transition from compressed representations to high-quality waveforms, avoiding the abrupt artifacts introduced by traditional clipping methods.
3Object-affected harmful factors
If traditional speech enhancement methods are used to remove background noise, then noise reduction is improved, but non-linear compression artifacts are introduced that reduce intelligibility
Solution Approach 1:
The patent replaces non-linear traditional speech enhancement methods with a probabilistic diffusion model that models the underlying distribution of clean speech. This approach naturally handles noise removal while preserving speech intelligibility by learning the statistical characteristics of clean speech rather than applying deterministic filtering operations.
Data Source
AI summary
Systems, methods, and apparatuses to restore degraded speech via a modified diffusion model are described. An exemplary system is specially configured to train a diffusion-based vocoder containing an upsampler, based on pairing original speech x and degraded speech mel-spectrum mT samples; train a deep convoluted neural network (CNN) upsampler based on a mean absolute error loss to match the estimated original speech {circumflex over (x)}′ outputted by the diffusion-based vocoder by extracting the upsampler, generating a reference conditioner, and generating a weighted altered conditioner cT<sub2>n</sub2>′. The system further optimizes speech quality to invert non-linear transformation and estimate lost data by feeding the degraded mel-spectrum mT through the CNN upsampler and feeding the degraded mel-spectrum mT through the diffusion-based vocoder. The system then generates estimated original speech {circumflex over (x)}′ based on the corresponding degraded speech mel-spectrum mT. Other related embodiments are described.