Multichannel Neural Speech Frontend for Interference-Robust ASR
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems struggle with robustness in the presence of background interference such as device echo, background noise, and competing speech, particularly in low signal-to-noise ratio conditions, and current methods often train these interference types in isolation, leading to sub-optimal performance.
Innovation Solution
A multichannel neural frontend speech enhancement model using a speech cleaner, self-attention blocks, and a masking layer to process multichannel noisy input signals, jointly trained with a backend ASR model to enhance speech features by removing background interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate ASR models are trained to handle different interference types (reverberation, background noise, competing speech) in isolation, then each model can be specialized for its specific condition, but the overall system complexity increases and performance in combined conditions deteriorates
Solution Approach 1:
The patent merges multiple specialized ASR models into a single unified ASR model that is jointly trained to handle multiple interference types simultaneously. The training process uses combined loss functions that incorporate reverberation loss, background noise loss, and competing speech loss, allowing one model to learn representations robust to all these conditions without requiring separate specialized models for each.
Solution Approach 2:
The unified ASR model is designed with universal capabilities to process audio signals under diverse interference conditions. By incorporating multi-channel neural frontend processing and joint training with multiple loss functions, the model achieves multi-functionality in handling reverberation, background noise, and competing speech, eliminating the need for multiple separate models while maintaining robust performance across all conditions.
2Ease of manufacture
If de-noising modules are trained independently from ASR modules, then each module can be optimized separately, but the overall system performance deteriorates due to lack of coordination
Solution Approach 1:
The patent combines the training of de-noising modules and ASR modules into a single joint training process. The neural network architecture integrates the multichannel frontend processing with the ASR decoder, and the loss function combines spectral loss for de-noising with ASR loss for recognition performance. This joint training ensures that both modules are optimized together and coordinated to maximize overall system performance rather than being trained independently.
Solution Approach 2:
The joint training framework implements feedback mechanisms where the ASR loss provides guidance for the de-noising module training, and vice versa. The spectral loss from the de-noising module influences the ASR module through shared representations, creating a feedback loop that continuously refines both modules' performance in coordination with each other, ensuring optimal overall system performance.
3Measurement precision
If multichannel neural frontend processing is applied to remove background interference, then speech recognition accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the audio processing into distinct functional components: multichannel frontend processing for interference removal, self-attention blocks for contextual modeling, and ASR decoding for recognition. This segmentation allows each component to be optimized independently while working together, reducing overall computational complexity by avoiding redundant processing and enabling parallel computation of independent modules.
Solution Approach 2:
The model employs dynamic adaptation mechanisms including adaptive noise cancellation algorithms that adjust to varying acoustic conditions in real-time. The self-attention blocks dynamically weight important temporal and spectral regions based on the input signal characteristics, allowing the system to adapt its computational focus to the current acoustic environment and reduce unnecessary processing in stable conditions.
Data Source
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AI summary
A multichannel neural frontend speech enhancement model (200) includes a speech cleaner (300), a stack of self-attention blocks (400) each having a multi-headed self attention mechanism, and a masking layer (240). The speech cleaner receives, as input, a multichannel noisy input signal (202) and a multichannel contextual noise signal (204), and generates, as output, a single channel cleaned input signal (340). The stack of self-attention blocks receives, as input, at an initial block of the stack of self-attention blocks, a stacked input (232) including the single channel cleaned input signal and a single channel noisy input signal (206), and generates, as output, from a final block of the stack of self-attention blocks, an un-masked output (480). The masking layer receives, as input, the single channel noisy input signal and the un-masked output, and generates, as output, enhanced input speech features (250) corresponding to a target utterance (12).