Sentiment Modulation via Waveform Reshaping for Real-Time Emotion Adaptation
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Solution Overview
Problem
Conventional mechanisms for sentiment modulation in conversation systems are inadequate as they fail to dynamically detect, modulate, and adapt emotions in real-time, often resulting in static and non-impactful emotional expressions that do not align with the listener's state of mind.
Innovation Solution
A method and system that generate sentiment and emotion vectors for sentences, create dependency vectors, and reshape waveforms to produce rephrased sentences based on user-defined emotional outputs, ensuring dynamic and context-specific emotional modulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static emotions are reflected through text of stored responses and offline communication, then the text can convey emotions, but the emotions cannot adapt to the listener's state of mind and situation
Solution Approach 1:
The system pre-processes and stores multiple rephrased versions of responses with different emotional modulations before interaction. When a listener's state is detected, the system can quickly retrieve and select from these pre-prepared alternatives, avoiding the need for complex real-time generation while achieving adaptability.
Solution Approach 2:
The system creates multiple copies of the same response content with different emotional characteristics (different rephrased sentences). These copies are stored and can be selected based on the listener's state, allowing emotion adaptation without changing the core information content.
2Adaptability or versatility
If conventional mechanisms use fixed and pre-defined modulation for sentences, then the modulation process is simple, but the modulation cannot be dynamic or context-specific
Solution Approach 1:
Multiple rephrased sentences with different emotional modulations are generated and stored in advance for each original sentence. During real-time conversation, the system only needs to retrieve and select from these pre-generated alternatives based on detected emotional context, maintaining high processing speed while achieving dynamic adaptation.
Solution Approach 2:
The system transitions from fixed, static emotion modulation to dynamic selection among multiple pre-generated emotional variants. The appropriate emotional modulation is selected in real-time based on the listener's detected state, enabling context-specific adaptation without sacrificing processing efficiency.
3Manufacturing precision
If bold text is used to stress a point, then the emphasis is clear, but the degree of boldness cannot be modulated
Solution Approach 1:
The system uses sentiment vectors with weighted values and waveform parameters to continuously adjust the intensity and type of emotional modulation. Instead of binary bold/not-bold formatting, the system varies parameters like sentiment weight, emotion intensity, and waveform characteristics to achieve precise control over emotional expression degrees.
Solution Approach 2:
The system introduces intermediate representations (sentiment vectors, emotion vectors, waveforms) between the original text and the final output. These intermediaries enable fine-grained control over emotional modulation by allowing incremental adjustments to sentiment weights and waveform parameters, achieving precise emotion intensity control.
4Measurement precision
If multiple rephrased sentences are generated based on reshaped waveform, then the emotional expression becomes more accurate, but the processing time increases
Solution Approach 1:
Multiple rephrased sentences with different emotional characteristics are generated and stored in advance for each original sentence. During real-time interaction, the system retrieves these pre-generated alternatives and selects the most appropriate one based on the listener's detected state, achieving accurate emotional expression without real-time generation delays.
Data Source
AI summary
A method and system for performing real-time sentiment modulation in conversation systems is disclosed. The method includes generating an impact table comprising a plurality of sentiment vectors and a plurality of emotion vectors associated with the plurality of sentences. The method further includes generating for each of the plurality of sentences, a dependency vector based on the associated sentiment vector and the associated emotion vector. The method further includes stacking the dependency vector generated to generate a waveform representing variance in sentiment and emotions across words within the plurality of sentences. The method further includes altering at least one portion of the waveform based on a desired emotional output to generate a reshaped waveform. The method further includes generating a set of rephrased sentences associated with the at least one portion, based on the reshaped waveform, the set of sentences, a user defined sentiment output.