Multi-Channel Transformer Acoustic Model for Low-Latency Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition systems in noisy environments face challenges due to stagewise training of neural beamformers without insight into downstream speech processing, leading to suboptimal performance in real-world conditions, and existing end-to-end approaches result in increased model size and latency.
Innovation Solution
A multi-channel transformer acoustic model that uses channel-wise and cross-channel attention mechanisms to directly predict acoustic units from multiple audio signals, integrating acoustic modeling operations and operating with low latency, suitable for on-device processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If stagewise training of neural beamformers is used, then device complexity is reduced, but speech recognition accuracy deteriorates in noisy environments
Solution Approach 1:
The patent combines acoustic modeling and beamforming operations into a single unified neural network model. The multi-channel transformer model processes multiple microphone inputs directly to predict acoustic units, eliminating the need for separate beamforming and ASR stages. This integration allows the model to learn optimal spatial filtering and speech recognition jointly, improving accuracy in noisy environments while maintaining computational efficiency through a single end-to-end training process.
2Reliability
If end-to-end approaches are used, then speech recognition accuracy improves, but model size and latency increase
Solution Approach 1:
The patent implements a segmented processing approach where the neural network processes audio in overlapping frames with incremental prediction. The model takes short audio segments as input and produces incremental acoustic unit predictions, allowing for lower latency compared to processing entire utterances at once. This segmentation enables real-time speech recognition while maintaining the accuracy benefits of end-to-end learning.
Solution Approach 2:
The model uses partial action by predicting acoustic units incrementally as audio frames are processed, rather than waiting for complete utterances. The system can provide preliminary recognition results based on partial input, reducing perceived latency. Additionally, the model processes more information than strictly necessary by considering multiple channels and temporal contexts, which improves accuracy without proportionally increasing latency.
3Reliability
If more microphones are added to handle noisy environments, then speech recognition reliability improves, but device complexity and computational cost increase
Solution Approach 1:
The multi-channel transformer model is designed to be universal and adaptable to different microphone configurations. The same model architecture can process inputs from 2 microphones or 8 microphones without requiring separate models or complex reconfiguration. The model automatically learns to exploit spatial relationships and noise patterns appropriate to the specific number and arrangement of microphones available, making the system universally applicable across different device form factors while maintaining robust performance in noisy environments.
Data Source
AI summary
A multi-channel transformer acoustic model that processes a plurality of audio signals output by microphones of a microphone array and outputs probabilities for acoustic units of an utterance represented in the audio signals. The audio signals represent the individual microphones' respective capturing of the utterance. The multi-channel model may perform self-attention on embeddings of the audio signals and then cross-channel attention across the attended audio signals. The cross-channel attention may involve processing of signals relative to each other to model the relationships across channels within and across time frames. The multi-channel model may include a transducer to perform processing frame-by-frame.