Speech Recognition Model Training via Contrastive Consistency Loss
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Solution Overview
Problem
Deep learning-based ASR models tend to overfit training data, leading to difficulties in generalizing unseen data, especially when the training data is not extensive enough.
Innovation Solution
The method involves generating a pair of positive audio data examples using a data augmentation module for each training utterance, projecting these examples into a contrastive loss space using a convolutional neural network (CNN), and determining a per-utterance consistency loss by averaging L2 distances between corresponding encoder outputs. This consistency loss is then used to update the parameters of the speech recognition model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data augmentation is applied to increase acoustic diversity, then generalization ability improves, but model complexity increases
Solution Approach 1:
The patent applies data augmentation techniques (adding noise, reverberation, time stretching) to the training data before model training. This preliminary transformation of training samples creates acoustically diverse versions of the same utterance, enabling the model to learn robust representations that generalize better to unseen data without modifying the model architecture itself.
Solution Approach 2:
The patent modifies acoustic parameters of training data through data augmentation - adjusting noise levels, reverberation characteristics, and temporal parameters. These parameter transformations create varied training examples from limited data, improving generalization while keeping the model structure fixed.
2Reliability
If training data size is increased to reduce overfitting, then generalization improves, but data acquisition cost increases
Solution Approach 1:
The patent creates multiple augmented copies of each training utterance by applying various transformations (noise addition, reverberation, time manipulation). These synthetic copies serve as additional training examples without requiring new recorded data, effectively increasing training data quantity from limited original samples.
Solution Approach 2:
By systematically varying acoustic parameters (signal-to-noise ratio, reverberation time, playback rate) of existing training utterances, the patent generates diverse training examples that simulate different recording conditions, thereby expanding the effective training dataset without additional data collection costs.
Data Source
AI summary
A method includes receiving audio data corresponding to an utterance and generating a pair of positive audio data examples. Here, each positive audio data example includes a respective augmented copy of the received audio data. For each respective positive audio data example, the method includes generating a respective sequence of encoder outputs and projecting the respective sequence of encoder outputs for the positive data example into a contrastive loss space. The method also includes determining a L2 distance between each corresponding encoder output in the projected sequences of encoder outputs for the positive audio data examples and determining a per-utterance consistency loss by averaging the L2 distances. The method also includes generating corresponding speech recognition results for each respective positive audio data example. The method also includes updating parameters of the speech recognition model based on a respective supervised loss term and the per-utterance consistency loss.