Language-Invariant Audiovisual Representation Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models that encode scenes into audiovisual representations often over-emphasize speech, leading to inaccurate generation of representations for similar scenes with different speech audio, which distracts from important scene-level correlations between visual and auditory channels.
Innovation Solution
Training machine-learning models to de-emphasize speech audio by generating training data with primary and dubbed language audio tracks, using convolutional neural network encoders and transformer models to project representations into a shared dimensional space, ensuring that similar scenes with different speech are positioned close together in the representational space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models emphasize speech audio when generating audiovisual representations, then speech-related information is captured accurately, but scene-level correlations between visual and auditory channels are distorted and representations of similar scenes with different speech become inaccurate
Solution Approach 1:
The audio representation is segmented into speech components and non-speech components (soundscapes). The model processes these separately, allowing speech to be recognized accurately while non-speech auditory information contributes to scene-level correlations without distortion.
Solution Approach 2:
Speech is extracted and isolated from the audio track as a separate component. This extracted speech representation is then processed independently from the non-speech audio, preventing speech from dominating or distorting the scene representation while still preserving speech information for recognition tasks.
2Ease of manufacture
If machine learning models focus on speech patterns in audio tracks, then speech-related features are enhanced, but the ability to recognize scene similarities with different speech audio is reduced
Solution Approach 1:
An intermediary processing stage separates speech from non-speech audio components. This intermediary representation allows the model to maintain speech features for extraction while simultaneously enabling language-invariant scene recognition by focusing on non-speech auditory-visual correlations that persist across different languages.
3Loss of information
If machine learning models encode audio and video separately, then modality-specific features are preserved, but integrated scene understanding is compromised
Solution Approach 1:
The separate audio and video representations are merged into a unified audiovisual representation. This merging integrates modality-specific features from both channels while creating a cohesive scene representation that captures correlations between visual and auditory information, improving overall scene understanding.
Data Source
AI summary
The disclosed computer-implemented methods and systems include training a machine-learning model to accurately generate representations of similar scenes from long-form videos that have semantically different speech audio. For example, the methods and systems described herein generate machine-learning model training data including video clips and corresponding audio spectrograms. To augment this data, the methods and systems described herein further include dubbed audio spectrograms with the training data such that each video clips corresponds with a primary language audio spectrogram and a secondary language audio spectrogram. By applying a machine-learning model to this training data, the systems and methods described herein teach the machine-learning model to de-emphasize speech audio when generating audio visual representations corresponding to scenes from long-form video. Various other methods, systems, and computer-readable media are also disclosed.