Cross-modal Sequence Distillation for Video Speech Recognition
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Solution Overview
Problem
Training a video speech recognition model is challenging due to the scarcity of high-quality labeled data, while large amounts of unsupervised video-audio data are readily available, making it difficult to achieve state-of-the-art performance without accurate labels.
Innovation Solution
A system that utilizes a trained audio speech recognition model to train a video speech recognition model using unlabeled video-audio data, where the audio model's parameters are kept fixed, and only the video model's parameters are adjusted, allowing for effective training even when the models are not temporally aligned or use different vocabularies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a video speech recognition model is trained using supervised learning with labeled data, then the model achieves high accuracy, but the requirement for large amounts of labeled video data becomes a bottleneck
Solution Approach 1:
The patent introduces an audio speech recognition model as an intermediary to generate pseudo-labels from unlabeled video-audio data. This mediator bridges the gap between available unlabeled data and the need for labeled training data, allowing the video speech recognition model to be trained indirectly through knowledge distillation from the audio model's predictions
Solution Approach 2:
The patent creates pseudo-labels by copying the output predictions from the trained audio speech recognition model and using them as target labels for training the video speech recognition model. This copying approach allows leveraging the audio model's learned patterns without requiring actual human-annotated video transcription data
2Ease of manufacture
If a video speech recognition model is trained in an unsupervised manner using unlabeled data, then the requirement for labeled data is eliminated, but achieving state-of-the-art performance becomes difficult
Solution Approach 1:
The patent performs preliminary training of an audio speech recognition model on audio data before using it to train the video speech recognition model. This preliminary action creates a knowledgeable teacher model that can provide high-quality pseudo-labels, enabling the subsequent unsupervised training of the video model to achieve state-of-the-art performance
Solution Approach 2:
The audio speech recognition model serves as an intermediary teacher that transfers knowledge to the video speech recognition model through knowledge distillation. This mediator enables the student model to learn from unlabeled data by leveraging the teacher's predictions, achieving high performance without direct supervision
3Adaptability or versatility
If knowledge distillation is performed between models with different vocabularies and temporal alignments, then the applicability of the method increases, but the complexity of handling mismatched representations increases
Solution Approach 1:
The patent segments the knowledge transfer process into independent components: the teacher audio model processes audio sequences independently, the student video model processes video sequences independently, and a separate alignment mechanism handles the mapping between them. This segmentation allows each model to operate with its own vocabulary and temporal structure while still enabling knowledge transfer
Solution Approach 2:
The patent introduces an alignment mechanism as an intermediary that bridges the gap between models with different vocabularies and temporal alignments. This mediator translates and matches representations from the teacher model to the student model's space, enabling knowledge distillation across modalities without requiring the models to share the same representation space
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a video speech recognition model having a plurality of model parameters on a set of unlabeled video-audio data and using a trained speech recognition model. During the training, the values of the parameters of the trained audio speech recognition model fixed are generally fixed and only the values of the video speech recognition model are adjusted. Once being trained, the video speech recognition model can be used to recognize speech from video when corresponding audio is not available.