Recurrent Model Feature Reconstruction for Speech Recognition Overfitting
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Solution Overview
Problem
Recurrent models in speech recognition often suffer from overfitting to training data, and multi-task learning approaches require expensive auxiliary tasks to prevent this, necessitating an improved method for enhancing model accuracy.
Innovation Solution
A multi-task learning system that includes a common encoder network, a primary network for minimizing Connectionist Temporal Classification (CTC) loss for speech recognition, and a sub-network for minimizing Mean Squared Error (MSE) loss for feature reconstruction, where the primary network receives both sets of output data from the encoder network, and the sub-network receives only one set, enabling feature reconstruction as an auxiliary task without manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recurrent models are used for speech recognition, then modeling capability is improved, but overfitting to training data occurs
Solution Approach 1:
The patent converts the harmful overfitting behavior into a beneficial feature reconstruction task. By training the model to reconstruct input features from hidden states, the system forces the model to learn meaningful representations rather than merely memorizing training data, thus transforming the overfitting problem into a regularization solution
Solution Approach 2:
The patent implements multi-task learning where the recurrent model simultaneously performs speech recognition and feature reconstruction. This multi-functionality prevents overfitting by requiring the model to learn generalizable features that serve both tasks, rather than optimizing for speech recognition alone
2Reliability
If multi-task learning is used to prevent overfitting, then model accuracy is improved, but expensive auxiliary task preparation is required
Solution Approach 1:
The patent employs self-supervised learning where the model learns to reconstruct its own input features without requiring external auxiliary tasks. The feature reconstruction task is automatically generated from the model's own hidden states, eliminating the need for manual auxiliary task design and preparation
Solution Approach 2:
The patent creates a simplified copy of the input features as a reconstruction target. Instead of requiring complex auxiliary tasks, the system generates reconstruction targets by copying and transforming the original input features through the model's hidden representations, providing an automatic and scalable regularization mechanism
Data Source
AI summary
A multi-task learning system is provided for speech recognition. The system includes a common encoder network. The system further includes a primary network for minimizing a Connectionist Temporal Classification (CTC) loss for speech recognition. The system also includes a sub network for minimizing a Mean squared error (MSE) loss for feature reconstruction. A first set of output data of the common encoder network is received by both of the primary network and the sub network. A second set of the output data of the common encode network is received only by the primary network from among the primary network and the sub network.