Audio Channel Augmentation with Impulse Responses for Low-Resource Speech
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Solution Overview
Problem
Existing AI models struggle to provide reliable performance in transcribing low-resource audio recordings due to unique channel characteristics like noise, interference, and channel distortions, and current solutions are either computationally expensive or lack flexibility in augmenting audio datasets with distinct channel characteristics.
Innovation Solution
A system and method that convert a parallel corpus of audio recordings into frequency domain features, extract a channel impulse response, and augment the channel characteristics of inference audio recordings using this response to match target characteristics, allowing machine learning models to process audio recordings with improved accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synthetic data is generated using additive computer algorithms to augment low-resource audio data, then speech recognition systems become more robust to noise and interference, but the solution lacks flexibility to augment audio datasets having unique channel characteristics
Solution Approach 1:
The patent introduces channel impulse response as an intermediary that captures the unique channel characteristics between source and target audio recordings. This intermediary enables flexible adaptation to different channel characteristics without requiring complete retraining or manual annotation, resolving the contradiction between reliability and adaptability
Solution Approach 2:
The patent changes the approach from adding noise directly to audio signals to modifying the channel impulse response parameters. By extracting and applying channel impulse response characteristics, the system can adapt to various channel conditions (different devices, environments, compression techniques) while maintaining speech recognition accuracy
2Reliability
If complicated AI models are used to process audio recordings and account for noise and interference, then speech recognition accuracy improves, but the models are computationally expensive and cannot be run on most CPUs
Solution Approach 1:
The patent extracts the channel characteristics as a separate channel impulse response component from the audio recordings. By isolating and pre-processing the channel characteristics, the main speech recognition model only needs to process the speech content, reducing computational complexity while maintaining accuracy
Solution Approach 2:
The patent performs preliminary extraction and analysis of channel impulse response characteristics before the main speech recognition processing. This pre-processing step separates channel effects from speech content, allowing simpler and more efficient models to be used during actual speech recognition while still accounting for noise and interference
3Reliability
If manually annotated noisy telephonic conversations are used for training, then models can learn from real-world conditions, but the process is difficult, time-consuming, and impractically expensive
Solution Approach 1:
The patent creates synthetic training data by copying and transforming existing clean audio recordings through extracted channel impulse responses. This copying approach generates realistic noisy telephonic conversation samples without requiring manual annotation, significantly reducing time and cost while maintaining training quality
Solution Approach 2:
The system uses the channel impulse response extracted from the audio data itself to generate augmented training samples. The data essentially annotates and augments itself through the channel characteristics already present in the recordings, eliminating the need for external manual annotation processes
Data Source
AI summary
The present disclosure provides a system (110) and a method (400) for augmenting channel characteristics of audio recordings. The system (110) receives a parallel corpus comprising a first set of audio recordings having a source channel characteristic and a second set of audio recordings having a target channel characteristic. The system (110) converts the parallel corpus into frequency domain features, extracts a channel impulse response based on the frequency domain features of the first and the second sets of audio recordings, and augments channel characteristics of an inference audio recording using the channel impulse response extracted from the parallel corpus.