GAN Encoder for Robust Speech Recognition
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Solution Overview
Problem
Current automatic speech recognition (ASR) systems face significant degradation due to variations in reverberations, ambient noise, and accents, and existing techniques struggle to induce invariance and scalability, particularly in non-stationary processes like accents, and require expensive and time-consuming data collection for high-quality datasets.
Innovation Solution
The use of a generative adversarial network (GAN) framework, specifically a sequence-to-sequence model with an encoder distance enhancer and a Wasserstein GAN enhancer, trains the encoder to produce indistinguishable embeddings between noisy and clean audio samples, achieving robustness without alignment or complex inference pipelines, and leveraging multitask learning to constrain the embedding space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data augmentation is used to train ASR models on varied conditions, then robustness to noise and reverberation improves, but data collection becomes expensive and time-consuming
Solution Approach 1:
The patent uses generative adversarial networks to synthesize noisy and reverberated audio samples from clean audio data, creating artificial training data without requiring physical data collection. The GAN generator creates realistic noisy audio copies that mimic real-world conditions, eliminating the need for expensive field recordings while maintaining robustness training effectiveness
Solution Approach 2:
The system performs self-augmentation by using its own clean audio data to generate noisy training samples through the GAN framework. Instead of relying on external data sources, the model serves itself by creating the diverse training conditions it needs, converting a single clean dataset into multiple noisy variants automatically
2Measurement precision
If hand-engineered front-ends are used to model reverberation and noise, then performance in specific operating regimes improves, but generalization to other modalities deteriorates
Solution Approach 1:
The patent transitions from fixed hand-engineered parameters to learnable parameters through neural networks. The GAN framework learns optimal noise and reverberation characteristics directly from data, allowing the system to adapt to different modalities and conditions without re-engineering, while maintaining precise modeling through learned parameters that capture complex acoustic transformations
Solution Approach 2:
The patent replaces hand-engineered acoustic modeling with data-driven generative adversarial networks. Instead of using physical models of reverberation and noise, the system uses neural networks to learn and generate these effects, substituting mechanical/acoustic engineering approaches with machine learning that generalizes across different operating conditions
3Reliability
If existing techniques are used to induce invariance for ASR, then robustness to specific distortions improves, but scalability to other effects deteriorates
Solution Approach 1:
The patent creates a universal GAN-based framework that can generate multiple types of audio distortions (noise, reverberation, Lombard reflex, accents) through a single system architecture. The generative model learns a comprehensive representation of acoustic transformations that can be applied to various effects, making the robustness training scalable across different distortion types without requiring separate specialized techniques for each effect
Data Source
AI summary
Described herein are systems and methods for a general, scalable, end-to-end framework that uses a generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained with the proposed approach enjoy improved invariance by learning to map noisy audio to the same embedding space as that of clean audio. Embodiments of a Wasserstein GAN framework increase the robustness of seq-to-seq models in a scalable, end-to-end fashion. In one or more embodiments, an encoder component is treated as the generator of GAN and is trained to produce indistinguishable embeddings between labeled and unlabeled audio samples. This new robust training approach can learn to induce robustness without alignment or complicated inference pipeline and even where augmentation of audio data is not possible.