Generalized ASR Frontend with Context Dropout for Echo and Noise
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automatic speech recognition (ASR) systems struggle with robustness in the presence of echo, background noise, and competing speech, as they are typically trained in isolation and lack practicality in handling simultaneous conditions.
Innovation Solution
A contextual frontend processing model is trained using a dropout strategy to jointly handle echo cancellation, speech enhancement, and voice separation, incorporating reference signals, noise context, and speaker embeddings, enabling improved performance even when these inputs are missing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a joint ASR model is trained to handle echo, background noise, and competing speech simultaneously, then robustness of ASR systems is improved, but training practicality deteriorates because not all conditions occur at the same time in real usage
Solution Approach 1:
The training approach dynamically adapts by using dropout strategies during training to simulate different real-world conditions. The model is trained with variable input configurations where contextual signals are randomly dropped to mimic actual usage scenarios, making the training process flexible and practical while maintaining robustness.
Solution Approach 2:
The model uses parameter changes through dropout probabilities and varying training configurations. By changing the presence/absence parameters of contextual signals during training and using different probability values for dropout, the model learns to handle diverse conditions without requiring all conditions to be present simultaneously, resolving the training practicality issue.
2Measurement precision
If multiple contextual signals (reference audio, noise context, speaker vectors) are used together in the model, then speech enhancement performance is improved, but model complexity increases
Solution Approach 1:
The contextual frontend processing model serves multiple functions simultaneously: it performs acoustic echo cancellation, speech enhancement, and voice separation using the same architectural framework. This multi-functional design improves speech enhancement accuracy without proportionally increasing complexity, as the model handles multiple tasks through a unified structure.
Solution Approach 2:
The model employs dropout strategies where contextual signals are partially present or absent during training with predetermined probabilities. This partial action approach allows the model to learn robust feature representations without requiring all contextual signals to be fully utilized, effectively managing complexity while maintaining enhancement accuracy.
3Measurement precision
If contextual signals are always present during training, then model performance on ideal conditions is improved, but adaptability to real-world conditions deteriorates where signals may be missing
Solution Approach 1:
The dropout strategy implements preliminary anti-action by intentionally removing contextual signals during training before deployment. This prepares the model in advance to handle missing signals in real-world conditions, preventing performance degradation when signals are absent during actual usage while maintaining high performance when signals are available.
Solution Approach 2:
The model performs preliminary learning through dropout training where it practices making predictions with incomplete contextual information. This preliminary action during training equips the model with the ability to adapt to missing signals in deployment, ensuring both ideal condition performance and robustness to signal absence.
Data Source
AI summary
A method for training a generalized automatic speech recognition model for joint acoustic echo cancellation, speech enhancement, and voice separation includes receiving a plurality of training utterances paired with corresponding training contextual signals. The training contextual signals include a training contextual noise signal including noise prior to the corresponding training utterance, a training reference audio signal, and a training speaker vector including voice characteristics of a target speaker that spoke the corresponding training utterance. The operations also include training, using a contextual signal dropout strategy, a contextual frontend processing model on the training utterances to learn how to predict enhanced speech features. Here, the contextual signal dropout strategy uses a predetermined probability to drop out each of the training contextual signals during training of the contextual frontend processing model.