Directional Speech Recognition With Multi-Path Echo Cancellation
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Solution Overview
Problem
Existing electronic devices struggle to accurately distinguish between multiple audio sources, particularly in noisy environments, leading to challenges in speech recognition and transcription, especially for hearing-impaired users and those with language barriers, and echo cancellation is inadequate.
Innovation Solution
The use of a multi-microphone array with multi-path acoustic echo cancellation and beam forming techniques to differentiate between audio sources, combined with an ASR component trained to recognize and attribute speech in multi-path AEC audio, enhances audio quality and accuracy in speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple microphones are used to receive audio from multiple sources, then the ability to distinguish between different audio sources is improved, but the complexity of the system increases
Solution Approach 1:
The audio processing is segmented into distinct functional components: multi-path AEC for echo removal, beamforming for spatial separation, and ASR for speech recognition. Each component handles a specific aspect of the audio processing pipeline, making the complex system more manageable and effective
Solution Approach 2:
Beamforming acts as an intermediary between the multi-microphone array and the ASR system, transforming raw multi-channel audio into directionally-separated audio streams that the ASR can process more effectively
2Reliability
If acoustic echo cancellation is applied to remove echoes from audio, then audio quality is improved, but the processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary echo cancellation processing on the multi-channel audio before passing it to the beamforming and ASR stages. By removing echoes early in the processing pipeline, subsequent stages work with cleaner audio signals, reducing their computational burden
Solution Approach 2:
The multi-path AEC operates in the multi-channel audio domain, treating echo cancellation as a multi-dimensional problem rather than simple single-channel processing. This allows simultaneous echo removal across multiple audio channels while preserving spatial information
3Measurement precision
If beam forming is used to segment audio into sectors, then the ability to distinguish audio sources is improved, but the device complexity increases
Solution Approach 1:
Beamforming creates directionally-specific audio streams with enhanced quality for each spatial sector. Each beamformed channel is optimized for capturing sound from a specific direction, providing local quality enhancement that improves source attribution accuracy
Data Source
AI summary
An example method of providing speech-to-text transcription includes receiving, at an electronic device, multiple channels of audio data from a plurality of microphones, where the multiple channels of audio data comprise speech from a user of the electronic device and speech from one or more other persons. The method also includes generating refined audio data by applying a multi-path acoustic echo cancellation (AEC) technique to the multiple channels of audio data. The method further includes generating directional audio data by applying beamforming to the refined audio data. The method also includes identifying, by inputting the directional audio data to an automatic speech recognizer (ASR), the speech from the user of the electronic device and the speech from the one or more other persons, and generating a textual transcription for the conversation.