Cross-Attention Fusion of Text and Audio for Spoken Sentiment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional sentiment classification models struggle to accurately capture the nuanced sentiment of spoken language due to their reliance on text data, which lacks the richness of features like pitch, tone, and frequency present in speech.
Innovation Solution
A multi-modal cross attention approach using generative adversarial networks (GANs) to generate synthetic audio samples and fuse textual and audio embeddings, enhancing sentiment prediction with audio attributes like pitch and tone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment classification models are used on text-based data, then the model structure remains simple and computationally efficient, but the accuracy of sentiment capture deteriorates due to inability to represent pitch, tone, and frequency information
Solution Approach 1:
The patent combines textual embeddings and audio embeddings into a unified multi-modal representation. The cross-attention mechanism merges text features and audio features (pitch, tone, frequency) to create fused embeddings that capture both linguistic and paralinguistic information, thereby improving sentiment analysis accuracy while maintaining a structured approach to handling multiple data types
Solution Approach 2:
The cross-attention mechanism serves as an intermediary that selectively integrates audio features with text features. It acts as a mediator that determines which audio attributes (pitch, tone, frequency) are relevant to specific text segments, enabling precise sentiment capture without directly combining all features in a complex manner
2Measurement precision
If multi-modal cross attention approach with GANs is used to fuse textual and audio embeddings, then sentiment prediction accuracy improves by incorporating audio features like pitch and tone, but the device complexity and computational requirements increase
Solution Approach 1:
The patent applies GANs in advance to generate synthetic audio samples that augment the training data. This preliminary action creates diverse audio embeddings with varied pitch, tone, and frequency characteristics before the main sentiment analysis process, allowing the model to learn from richer audio features without requiring extensive real-world audio data
Solution Approach 2:
The patent transitions from unimodal text-based sentiment analysis to multi-modal analysis by adding the audio dimension. Cross-attention mechanisms process audio embeddings alongside text embeddings, enabling the system to capture paralinguistic features (pitch, tone, frequency) that provide additional sentiment information beyond what text alone can convey
Data Source
AI summary
A method for managing sentiment analysis includes obtaining, by a data classification system, raw data, associated with a meeting, wherein the raw data the comprises a textual transcript and an audio file of the meeting, applying a generative adversarial network (GAN) to the audio file to obtain audio embeddings and extracted text, applying the textual transcripts and the extracted text to a trained language model to obtain textual embeddings, applying the audio embeddings and the textual embeddings to a multi-modal cross attention module to obtain a fused embedding, performing a sentiment classification on the fused embedding to obtain a sentiment prediction, and implementing a remediation on the data classification system using the sentiment prediction.