Microphone Style Transfer Model for Robust Speech Recognition
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Solution Overview
Problem
Existing audio recognition models suffer from significant performance degradation due to domain shift caused by microphone variability, with existing methods either being limited to the cepstral domain, requiring multiple microphones during training, or introducing computational overhead.
Innovation Solution
A machine-learned microphone model that performs one-shot microphone style transfer by processing input audio data with impulse response, power-frequency, filtering, and clipping models to generate target audio data, enabling robustness to microphone variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CycleGAN is used to learn mapping between microphones, then it can model microphone transformations without paired data, but it requires several minutes of unpaired training data per microphone and introduces significant computational overhead during deployment
Solution Approach 1:
The patent pre-trains a universal microphone model during system initialization that can generalize across multiple microphone types. This preliminary action eliminates the need for time-consuming per-microphone training during deployment, as the model is already prepared to handle various microphone transformations without requiring several minutes of training data for each specific microphone.
Solution Approach 2:
The patent creates a universal microphone model that serves multiple microphone types simultaneously rather than training separate models for each microphone. This multi-functional approach allows a single model to handle transformations across diverse microphone types, reducing both training time and computational overhead during deployment while maintaining adaptability to different microphone characteristics.
2Adaptability or versatility
If CycleGAN is used to learn mapping between microphones, then it can model microphone transformations, but it introduces significant computational overhead during deployment due to training separate models for every microphone type
Solution Approach 1:
The patent merges the functionality of multiple separate microphone-specific models into a single universal microphone model. Instead of training and deploying separate CycleGAN models for each microphone type, the patent combines them into one unified model that handles all microphone transformations, significantly reducing computational overhead during deployment while maintaining the ability to model diverse microphone characteristics.
Solution Approach 2:
The patent creates a universal microphone model that serves multiple microphone types simultaneously rather than training separate models for each microphone. This multi-functional approach allows a single model to handle transformations across diverse microphone types, reducing both training time and computational overhead during deployment while maintaining adaptability to different microphone characteristics.
3Reliability
If additive corrections in the cepstral domain are used, then model robustness to microphone variability is improved, but the method is compatible only with applications operating on inputs in the cepstral domain
Solution Approach 1:
The patent introduces a universal microphone model as an intermediary layer between the audio input and the application-specific processing. This mediator model performs microphone variability compensation in a domain-agnostic manner, producing corrected audio outputs that can be fed into any downstream application regardless of whether it operates in the cepstral domain or other domains, thus maintaining both robustness and broad compatibility.
Data Source
AI summary
Example implementations of the present disclosure relate to machine learning for microphone style transfer, for example, to facilitate augmentation of audio data such as speech data to improve robustness of machine learning models trained on the audio data. Systems and methods for microphone style transfer can include one or more machine-learned microphone models trained to obtain and augment signal data to mimic characteristics of signal data obtained from a target microphone. The systems and methods can include a speech enhancement network for enhancing a sample before the style transfer. The augmentation output can then be utilized for a variety of downstream tasks.