Vehicle Audio Signal Processing for Speech Recognition Noise
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Solution Overview
Problem
Existing voice-activated vehicle systems face challenges in accurately processing voice commands due to distortion from non-speech related sounds, which affects the clarity and decipherability of voice commands and the overall functionality of speech recognition and dialogue control systems.
Innovation Solution
The use of sound-related vehicle information, such as engine RPM, HVAC settings, and external noise levels, to generate an interference profile record, which is then used to modify audio signals and adapt speech recognition and dialogue systems, applying filters and acoustic models to enhance speech recognition and audio output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If non-speech related sounds are present in the vehicle environment, then the audio signal contains more information about the vehicle state, but the speech recognition accuracy deteriorates due to noise distortion
Solution Approach 1:
The audio signal is segmented into speech components and non-speech noise components using acoustic models trained on vehicle-specific noise characteristics. This segmentation allows the system to separate and process speech signals independently from vehicle noises such as engine sounds, HVAC systems, and road noise.
Solution Approach 2:
Acoustic models serve as intermediaries between the raw audio signal and the speech recognition system. These models are trained on vehicle-specific noise data and act as a mediator to filter and preprocess the audio signal, removing vehicle-related noises while preserving speech content before it reaches the speech recognition engine.
2Measurement precision
If vehicle sound information is used to modify audio signals, then speech clarity is improved, but system complexity increases due to additional processing requirements
Solution Approach 1:
Acoustic models are pre-trained offline on extensive vehicle-specific noise data collected from various operating conditions. This preliminary training phase prepares the models in advance, so that during actual speech recognition, the pre-trained models can quickly and efficiently filter noise without requiring complex real-time processing or extensive computational resources.
Solution Approach 2:
The system adjusts audio signal parameters such as gain, frequency response, and filtering characteristics based on the output from acoustic models. These parameter changes are dynamically applied to compensate for vehicle noise conditions, improving speech clarity through relatively simple signal processing operations rather than complex algorithmic modifications.
Data Source
AI summary
Sound related vehicle information representing one or more sounds may be received in a processor associated with a vehicle. The sound related vehicle information may or may not include an audio signal. An audio signal output to a passenger may be modified based on the sound related vehicle information.