Emotion Classification in Interactive Dialog Systems
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Solution Overview
Problem
Interactive dialog systems lack the ability to effectively incorporate emotional content into their outputs, leading to a less engaging user experience, as they primarily focus on semantic content without considering emotional aspects.
Innovation Solution
An apparatus that combines user input data, such as device usage patterns and profile information, with algorithms to select an appropriate emotion type code for output statements, enabling the generation of synthesized speech with emotional characteristics, using either rules-based or machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If emotional content is incorporated into dialog system outputs, then user engagement and naturalness of conversation is improved, but system complexity increases due to need for emotion classification and synthesis mechanisms
Solution Approach 1:
The dialog system is segmented into distinct functional modules: emotion classification module that analyzes user input to determine emotional state, dialog management module that processes semantic content, and speech synthesis module that combines both to generate emotionally-inflected output. This segmentation allows each module to specialize in one aspect while maintaining overall system manageability.
Solution Approach 2:
An emotion classification module acts as an intermediary between user input and dialog output, analyzing emotional content from user speech or text and translating it into structured emotion data that the speech synthesis module can utilize. This intermediary layer decouples the complexity of emotion recognition from the dialog generation process.
2Measurement precision
If multiple data sources including device usage patterns are analyzed to determine emotion type, then accuracy of emotion classification is improved, but processing time and computational resources increase
Solution Approach 1:
Device usage patterns, calendar data, and user profile information are pre-processed and stored in structured formats before being needed for emotion classification. This preliminary preparation allows the emotion classification module to quickly retrieve and analyze relevant data without performing complex processing in real-time, thus improving both accuracy and reducing processing delays.
Solution Approach 2:
The system analyzes only the most relevant data sources needed for accurate emotion classification rather than processing all available data. For example, it may prioritize recent device usage patterns over historical data, or focus on specific emotion indicators based on the current dialog context, thereby reducing computational overhead while maintaining classification accuracy.
3Ease of operation
If synthesized speech includes emotional characteristics, then user experience and naturalness is improved, but computational resources and processing power are consumed
Solution Approach 1:
The speech synthesis module modifies acoustic parameters such as pitch, tone, tempo, and volume based on the classified emotion type to generate emotionally-inflected speech without requiring complex neural network inference. For example, happy emotions may increase pitch and tempo, while sad emotions may decrease them, achieving natural emotional expression through parameter adjustment rather than computationally intensive generation.
Data Source
AI summary
Techniques for selecting an emotion type code associated with semantic content in an interactive dialog system. In an aspect, fact or profile inputs are provided to an emotion classification algorithm, which selects an emotion type based on the specific combination of fact or profile inputs. The emotion classification algorithm may be rules-based or derived from machine learning. A previous user input may be further specified as input to the emotion classification algorithm. The techniques are especially applicable in mobile communications devices such as smartphones, wherein the fact or profile inputs may be derived from usage of the diverse function set of the device, including online access, text or voice communications, scheduling functions, etc.