Multilingual ASR Joint Training Without Separate Pretraining
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Solution Overview
Problem
Existing automatic speech recognition (ASR) models face challenges in generalizing to unseen data due to overfitting when training data is insufficient, and multilingual models suffer from imbalanced language representation and issues with pretrained checkpoint selection.
Innovation Solution
A joint unsupervised and supervised training (JUST) process that combines unsupervised loss derived from cross-entropy and self-supervised loss with a supervised loss from predicted speech recognition hypotheses and ground-truth transcriptions to train ASR models, eliminating the need for a separate pretraining stage and addressing catastrophic forgetting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ASR models are trained on larger training datasets, then the accuracy of the ASR model is improved, but the device complexity and training time increase
Solution Approach 1:
The training process is segmented into two distinct stages: unsupervised pretraining on large amounts of unlabeled audio data to learn general acoustic patterns, followed by supervised fine-tuning on smaller labeled datasets to achieve accurate transcriptions. This segmentation allows the model to leverage large datasets for foundational learning while maintaining manageable complexity in the supervised stage.
Solution Approach 2:
The model performs preliminary unsupervised pretraining before supervised training, where it learns general acoustic representations and language patterns from unlabeled data. This preliminary action prepares the model to achieve superior accuracy with less supervised data, effectively reducing the complexity burden of requiring extensive labeled datasets.
2Adaptability or versatility
If multilingual ASR models are trained to handle multiple languages, then the adaptability is improved, but the language representation becomes imbalanced
Solution Approach 1:
The training process applies local quality by treating different languages differently based on their characteristics and data availability. The unsupervised pretraining stage learns universal acoustic patterns that are language-agnostic, while the supervised fine-tuning stage adapts to specific language characteristics. This allows the model to maintain balanced representation across multiple languages by adjusting the quality and type of training data for each language rather than treating all languages uniformly.
3Quantity of substance
If a separate pretraining stage is implemented, then the model can learn from unlabeled data, but the training process becomes more complex
Solution Approach 1:
The patent merges the pretraining and supervised training processes into a unified framework where unsupervised and supervised objectives are combined in a single training loop. The model simultaneously optimizes for both unsupervised acoustic representation learning and supervised transcription accuracy, eliminating the need for separate pretraining and fine-tuning stages while still leveraging unlabeled data effectively.
Data Source
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AI summary
A method (400) includes receiving audio features (110) and generating a latent speech representation (212) based on the audio features. The method also includes generating a target quantized vector token (312) and a target token index (314) for a corresponding latent speech representation. The method also includes generating a contrastive context vector (322) for a corresponding unmasked or masked latent speech representation (212u, 212m) and deriving a contrastive self-supervised loss (355a) based on the corresponding contrastive context vector and the corresponding target quantized vector token. The method also includes generating a high-level context vector (334) based on the contrastive context vector and learning to predict the target token index using a cross-entropy loss (355b) based on the target token index. The method also includes predicting speech recognition hypotheses (342) and training a multilingual ASR model (200) using an unsupervised loss (355) and a supervised loss (365).