Multimodal Emotion Estimation With Speech-Text Cross-Attention
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Solution Overview
Problem
Existing emotion recognition technologies struggle to accurately characterize emotional states due to the inconsistency and heterogeneity of audio and text data, leading to challenges in effectively fusing these modalities and ignoring intra- and inter-modal interactions.
Innovation Solution
A multimodal emotion recognition model that utilizes a speech emotion representation (SER) and text emotion representation (TER) model, combined with a representation fusion (RF) model, employing cross-attention mechanisms to encode and fuse speech and text features, thereby enhancing the accuracy of emotion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-modal emotion recognition is used, then the system is simpler and easier to implement, but it cannot accurately characterize emotional states due to incomplete emotion information
Solution Approach 1:
The patent combines multiple modalities (audio, text, facial expressions, body language) into a unified emotion recognition system. The multi-modal fusion architecture integrates features from different sources to comprehensively characterize emotional states, resolving the limitation of single-modal approaches while managing complexity through structured feature fusion mechanisms.
Solution Approach 2:
The system creates a composite representation of emotional states by integrating heterogeneous data types (acoustic features, text features, visual features) into a unified emotion vector. This composite approach leverages the complementary strengths of different modalities to achieve more accurate emotion characterization than any single modality alone.
2Measurement precision
If audio and text data are fused, then comprehensive emotion information is achieved, but great inconsistency between modalities causes difficulties in effective fusion
Solution Approach 1:
The patent introduces intermediate processing layers including modality-specific encoders and a fusion layer with attention mechanisms. These intermediary components transform heterogeneous audio and text data into compatible feature representations, enabling effective fusion despite modality inconsistencies. The attention mechanism acts as a mediator that dynamically weights contributions from different modalities based on their relevance to the current emotional state.
Solution Approach 2:
The system dynamically adjusts fusion parameters including attention weights, fusion depth, and feature selection based on the characteristics of input data. This adaptive parameter adjustment allows the model to optimize the fusion process for different emotional contexts and data quality conditions, resolving the inconsistency between modalities through flexible parameter control.
3Measurement precision
If cross-attention mechanisms are employed, then intra- and inter-modal interactions are learned effectively, but the model complexity increases
Solution Approach 1:
The patent segments the attention mechanism into distinct components: intra-modal attention for each modality and inter-modal attention for cross-modality interactions. This segmentation allows independent optimization of each attention component and enables selective application based on computational resources available, managing complexity while maintaining interaction learning capability.
Solution Approach 2:
The system implements dynamic attention mechanisms that adaptively adjust the strength and scope of intra- and inter-modal interactions based on the input data characteristics. This dynamic approach allows the model to focus computational resources on the most relevant modality interactions for each specific emotional recognition task, achieving effective interaction learning with controlled complexity.
Data Source
AI summary
An apparatus capable of estimating emotion includes an SER model processing unit that inputs an acquired speaker identity feature and speaker speech data feature to a predetermined SER model, and outputs a phonetic feature query, a phonetic feature key, and a phonetic feature value through a process, a TER model processing unit that outputs a text feature query, a text feature key, and a text feature value of the text feature through a process from transferred text corresponding to the speaker identity feature and the speaker speech data feature, and an RF model processing unit that inputs the output phonetic feature query, phonetic feature key, and phonetic feature value of the speaker and the output text feature query, text feature key, and text feature value to a predetermined RF model, and outputs a probability for each emotion category of the speaker by applying a cross-attention mechanism.