Joint Acoustic Visual Processing for Cross-Modal Retrieval
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) systems require extensive training data and expert knowledge, limiting their applicability to only a few languages, and existing approaches struggle to associate audio and image data without semantic annotation, hindering cross-modal query and retrieval processes.
Innovation Solution
A joint acoustic and visual processing method that uses deep neural networks to associate images with audio signals without requiring semantic annotation, enabling cross-modal similarity processing by configuring image and audio processors to produce comparable numerical representations for similarity evaluation, allowing for query-based retrieval and annotation without human annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ASR systems use large amounts of training data and expert knowledge, then speech recognition accuracy is improved, but the complexity of the system and the cost of accumulating resources increase immensely
Solution Approach 1:
The system performs self-service by automatically learning acoustic units and language structures from untranscribed audio data without requiring manual annotation or expert knowledge. The unsupervised learning algorithms automatically discover repetitions of word-like units, part-of-speech tags, and linguistic patterns, eliminating the need for human annotators and reducing system complexity.
Solution Approach 2:
The patent replaces the mechanical process of manual data annotation and expert knowledge curation with automated computational processes. Machine learning models automatically process raw audio data to extract linguistic features, substituting human expert labor with algorithmic processing that scales more efficiently.
2Measurement precision
If semantic annotation is used to link images and audio, then the accuracy of cross-modal association is improved, but the requirement for human-annotated training data increases
Solution Approach 1:
The system performs self-service by automatically learning to associate images and audio through unsupervised learning from unannotated data. The model discovers correlations between visual and acoustic modalities without requiring human-provided semantic labels, automatically generating its own understanding of cross-modal relationships.
Solution Approach 2:
The system performs preliminary action by pre-training on large amounts of unannotated image-audio pairs to learn general cross-modal associations before fine-tuning on specific tasks. This preliminary learning from raw data establishes foundational representations that transfer to annotated tasks, reducing the need for extensive labeled training data.
3Adaptability or versatility
If unsupervised techniques are used to learn acoustic units from untranscribed audio, then the applicability to more languages is improved, but the precision of language learning decreases
Solution Approach 1:
The system applies segmentation by breaking down the continuous audio signal into discrete acoustic units through unsupervised clustering. It identifies repetitions and patterns in raw audio to segment word-like units, phonemes, and linguistic structures without pre-defined boundaries, enabling adaptation to any language while maintaining learning precision through iterative refinement.
Data Source
AI summary
An approach to joint acoustic and visual processing associates images with corresponding audio signals, for example, for the retrievals of images according to voice queries. A set of paired images and audio signals are processed without requiring transcription, segmentation, or annotation of either the images or the audio. This processing of the paired images and audio is used to determine parameters of an image processor and an audio processor, with the outputs of these processors being comparable to determine a similarity across acoustic and visual modalities. In some implementations, the image processor and the audio processor make use of deep neural networks. Further embodiments associate parts of images with corresponding parts of audio signals.