In-Vehicle Voice Interface Using Visual Context for Accurate Commands
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Solution Overview
Problem
Current speech processing systems lack human-level responsiveness and intelligence, struggling with complex processing pipelines that lead to errors in translating pressure fluctuations in the air into parsed commands, and are often not practical for real-world devices due to resource constraints.
Innovation Solution
A client-server architecture that extracts audio and visual features from both audio and image data using neural networks, with a linguistic model on the server device to parse utterances, reducing data size and enabling implementation on a range of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex processing pipelines are used to translate pressure fluctuations into parsed commands, then speech processing capability is improved, but error rate increases and human-level responsiveness is not achieved
Solution Approach 1:
The patent combines audio data and image data into a unified processing framework. The neural network jointly processes both modalities to parse utterances, merging previously separate audio-only and visual processing pipelines into an integrated system that leverages complementary information from both sensors to improve accuracy and reliability simultaneously.
Solution Approach 2:
The patent introduces visual features as an intermediary that mediates between raw audio data and parsed commands. The image data provides contextual information that disambiguates audio inputs, acting as a bridge that helps the system resolve uncertainties in speech recognition and achieve more accurate command translation.
2Measurement precision
If full audio and image data are transmitted to the server, then processing accuracy is improved, but data transmission size and bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential features from audio and image data before transmission. The client device processes raw sensor data locally to extract salient audio features and visual features, transmitting only these compressed feature representations to the server. This extraction process retains the information necessary for accurate utterance parsing while dramatically reducing data transmission volume.
Solution Approach 2:
The patent transforms raw audio and image data into different parameter representations (features) that are more suitable for transmission and processing. By changing the parameter space from raw pixel values and audio waveforms to extracted feature vectors, the system maintains processing accuracy while reducing data size and transmission requirements.
3Productivity
If high-performance computing resources are used for speech processing, then processing speed and accuracy are improved, but device cost and complexity increase
Solution Approach 1:
The patent segments the processing workload between client and server devices. The client device performs local preprocessing to extract features from audio and image data, while the server device performs the computationally intensive utterance parsing. This segmentation allows each device to operate within its resource constraints while achieving high overall processing speed and accuracy.
Solution Approach 2:
The patent performs preliminary processing of audio and image data on the client device before transmission to the server. By extracting features and preparing data in advance, the system reduces the computational burden on the server and enables faster end-to-end processing. This preliminary action allows high-performance processing to be distributed across multiple devices rather than concentrated in a single high-end system.
Data Source
AI summary
A driver interface for use within an automobile provides responses to voice commands issued for example by a driver of the automobile. The interface includes a camera and microphone for capturing image data such as gestures and audio data from the automobile driver. The image data and audio data are processed to extract image and linguistic features from the image and audio data, which image and linguistic features are processed to interpret and infer a meaning of the voice command.