Self-Supervised Speech Representation Learning
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Solution Overview
Problem
Current speech recognition systems require large amounts of labeled training data, which is scarce for many languages, limiting their availability and accuracy, especially for the over 7,000 languages worldwide where data collection is difficult.
Innovation Solution
A computing system uses self-supervised learning to pre-train models on unlabeled speech data, followed by fine-tuning with a small amount of labeled data, employing convolutional neural networks and transformer networks to generate contextualized representations that can handle cross-lingual speech tasks, enabling speech recognition with minimal labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition systems use supervised learning with labeled data, then recognition accuracy can be achieved, but the requirement for large amounts of labeled training data becomes a bottleneck for languages with scarce resources
Solution Approach 1:
The system performs self-supervised pre-training on unlabeled speech data before fine-tuning with labeled data. This preliminary action allows the model to learn general speech representations from abundant unlabeled data, reducing the subsequent need for labeled data while maintaining recognition accuracy
Solution Approach 2:
The system uses self-supervised learning where the model generates its own training signals from unlabeled data through masking and reconstruction tasks. This self-service mechanism eliminates the dependency on externally provided labeled data, allowing the system to bootstrap learning from raw speech signals alone
2Adaptability or versatility
If speech recognition systems are trained on multiple languages with different data availability, then cross-lingual capability is improved, but the complexity of handling data imbalance across languages increases
Solution Approach 1:
The system trains a universal model on multilingual unlabeled data that learns language-agnostic speech representations. This universal model can be adapted to any language through fine-tuning, providing cross-lingual capability without requiring separate models for each language and simplifying the handling of data imbalance
Solution Approach 2:
The system changes the training paradigm from language-specific supervised learning to language-agnostic self-supervised learning. By changing the objective function to mask-and-reconstruct tasks that are language-independent, the system can uniformly process multiple languages with different data availability without complex balancing mechanisms
Data Source
AI summary
In one embodiment, a method includes generating audio segments from a speech signal, generating latent representations that respectively correspond to the audio segments, the latent representations comprising a first subset and a second subset, generating quantized representations that respectively correspond to the latent representations, masking the second subset of the latent representations, using a machine-learning model to process the first subset of the latent representations and the masked second subset of the latent representations to generate contextualized representations that respectively correspond to the latent representations, pre-training the machine-learning model based on comparisons between (1) a subset of the contextualized representations that respectively correspond to the masked second subset of the latent representations and (2) a subset of the quantized representations that respectively correspond to the masked second subset of the latent representations, and training the pre-trained machine-learning model to perform a speech analysis task.