In-Vehicle Speech Output Enrichment Using Cabin Visual Context
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Solution Overview
Problem
Existing speech dialogue systems in vehicles lack the ability to enrich speech outputs with contextually relevant information beyond the spoken content, failing to provide a human-like interaction experience.
Innovation Solution
An imaging sensor, such as an interior camera, is used to analyze the passenger compartment, categorizing objects and people, and semantically link these categories with speech inputs to enhance speech outputs with contextually relevant keywords or phrases, utilizing a self-learning mechanism to adapt to user preferences and situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speech recognition starts at the touch of a button or via a code word, then the system can reliably identify when speech input begins, but the interaction becomes less natural and less engaging for the user
Solution Approach 1:
The system performs preliminary actions by continuously monitoring the acoustic environment and analyzing speech patterns before formal speech recognition is triggered. This allows the system to detect intent and prepare for interaction naturally, without requiring explicit activation commands, thereby improving interaction naturalness while maintaining reliable speech input detection through continuous background analysis
2Device complexity
If only speech content is analyzed, then the system remains simple and computationally efficient, but the speech output lacks contextual enrichment and human-like quality
Solution Approach 1:
The system merges speech content analysis with visual scene analysis by integrating data from microphones and cameras. The speech recognition module processes verbal input while the visual processing module analyzes the passenger compartment imagery, and both streams are combined to enrich speech output with contextual information about objects, people, and environmental conditions, creating a more human-like interaction experience
Solution Approach 2:
The system implements multi-functionality by using the imaging sensor not only for visual analysis but also for contextual enrichment of speech outputs. The same visual data that identifies objects and people in the passenger compartment is utilized to generate relevant keywords and formulations that enhance the speech output, allowing a single sensor system to serve multiple purposes and reduce overall system complexity
3Ease of operation
If the speech output is enriched with multiple categories of keywords and formulations, then the interaction becomes more human-like and engaging, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary action by pre-processing and categorizing visual data from the imaging sensor into structured information about objects, people, and environmental conditions. This pre-categorized visual context is stored and readily available when speech recognition occurs, allowing the speech output enrichment to draw from pre-organized data rather than performing full analysis in real-time, thereby reducing processing time while maintaining high interaction quality
Solution Approach 2:
The system applies partial action by selectively enriching speech outputs with only the most relevant keywords and formulations based on the current context and speech input type. Rather than always applying full enrichment with all available categories, the system intelligently selects which visual context elements to incorporate, reducing computational load and processing time while maintaining engaging and human-like interaction quality for each specific speech scenario
Data Source
AI summary
A method for generating speech outputs in a vehicle in response to a speech input involves recording, in addition to the speech input, additional information by at least one sensor. Afterwards an analysis of the speech input and the sensor data is performed and which is used as a basis for the speech output. At least one imaging sensor is used as the at least one sensor and it records the passenger compartment. Identified objects or people are assigned to predetermined categories. The speech output is produced based on the analysis results and is enriched with keywords or formulations matching one of the categories.