In-Vehicle Speech Processing Using Visual Speaker Features
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Solution Overview
Problem
Current speech processing systems in vehicles face challenges such as limited processing resources, high noise levels, and complex acoustic environments, leading to difficulties in accurately transcribing and parsing human utterances, especially in environments with multiple speakers and safety constraints.
Innovation Solution
The use of both audio and image data to generate a speaker feature vector based on facial characteristics, which is then integrated into the speech processing pipeline to improve accuracy and robustness, leveraging visual information to enhance the processing of audio data and overcome noise and multiple speaker challenges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech processing is performed using only audio data in a vehicle environment, then the system complexity is low, but the transcription accuracy deteriorates due to noise and multiple speakers
Solution Approach 1:
The patent combines audio data and image data into a unified speech processing system. The audio capture device and image capture device are integrated to process speech from multiple modalities simultaneously, improving transcription accuracy in noisy vehicle environments by merging complementary information sources.
Solution Approach 2:
The patent introduces a speaker feature vector as an intermediary element that bridges audio and visual data. This feature vector, derived from image data showing facial characteristics, serves as a mediator to enhance audio processing by providing visual speaker identification information that disambiguates speech sources in multiple-speaker scenarios.
2Reliability
If audio data alone is used for speech processing, then the processing speed is fast, but the reliability deteriorates in noisy environments with multiple speakers
Solution Approach 1:
The system merges audio data from an audio capture device with image data from an image capture device to process speech. This multi-modal combination improves reliability in noisy vehicle environments by cross-validating speech information across different sensory modalities, making the system more robust to environmental interference.
Solution Approach 2:
The speaker feature vector acts as an intermediary that enhances reliability by providing visual verification of speaker identity. This feature vector, extracted from image data, mediates between the audio signal and the final speech recognition output, ensuring that the correct speaker is identified even when audio alone is ambiguous.
3Measurement precision
If visual information is integrated into speech processing, then the accuracy in identifying speakers improves, but the processing time increases
Solution Approach 1:
The system performs preliminary extraction of speaker feature vectors from image data before the main speech recognition process. By pre-processing the visual information to create compact speaker identifiers in advance, the system reduces the computational burden during real-time speech processing, thereby minimizing processing time while maintaining identification accuracy.
Solution Approach 2:
The patent extracts only the essential speaker identification features from image data, rather than processing entire video frames. This selective extraction of relevant visual information (such as facial characteristics) reduces processing complexity and time while preserving the key information needed for accurate speaker identification.
Data Source
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AI summary
Systems and methods for processing speech are described. Certain examples use visual information to improve speech processing. This visual information may be image data obtained from within a vehicle. In examples, the image data features a person within the vehicle. Certain examples use the image data to obtain a speaker feature vector for use by an adapted speech processing module. The speech processing module may be configured to use the speaker feature vector to process audio data featuring an utterance. The audio data may be audio data derived from an audio capture device within the vehicle. Certain examples use neural network architectures to provide acoustic models to process the audio data and the speaker feature vector.