Speech Model Personalization via Federated Environmental Noise Augmentation
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Solution Overview
Problem
Automatic speech recognition systems face challenges in handling variations in accent, vocabulary, and background noise across different users, making it difficult to train a single model that provides accurate transcriptions and personalizes speech recognition models effectively, especially when there is a limited amount of user-specific audio data available.
Innovation Solution
The method involves training a speech model using a fixed set of training utterances augmented with noisy audio data sampled from the environment, which includes de-noising and pairing with corresponding transcriptions to generate noisy audio samples, and then updating the model parameters using a loss term based on the probability distribution of speech recognition hypotheses. This process can be done locally on a voice-enabled device and may also involve federated learning with a central server to aggregate updates from multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single speech recognition model is used to handle all users, then device complexity is reduced, but speech recognition accuracy deteriorates due to variations in accent, vocabulary, and background noise
Solution Approach 1:
The system segments the speech recognition model into two parts: a base model that handles general speech recognition and user-specific adapters that are personalized for each user. This allows the system to maintain a single device while achieving both low complexity and high accuracy through modular architecture.
Solution Approach 2:
The system applies local quality by maintaining a general base model for all users while introducing user-specific adaptations through personalized adapters trained on individual user data. This enables the system to handle global variations efficiently while capturing local user characteristics for improved accuracy.
2Measurement precision
If speech models are trained from scratch on user-specific data, then speech recognition accuracy is improved, but the amount of labeled data required and training time increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training a base model on large-scale general speech data before deploying it to user devices. This pre-trained model serves as a strong foundation that requires minimal user-specific data to adapt, thereby reducing the labeled data burden while maintaining high accuracy.
Solution Approach 2:
The system uses parameter changes by fine-tuning only the adapter parameters rather than retraining the entire model from scratch. This approach allows the model to adapt to user-specific characteristics with minimal data while preserving the knowledge learned during pre-training.
3Reliability
If noisy audio data from the environment is used for training, then speech model robustness is improved, but training data quality deteriorates due to background noise
Solution Approach 1:
The system converts the harmful effect of background noise into a benefit by using noisy environmental audio data to train the speech model. The model learns to tolerate and filter out background noise, thereby improving robustness while the de-noising preprocessing maintains acceptable training data quality.
Solution Approach 2:
The system introduces an intermediary de-noising process that processes the noisy audio data before it is used for training. This intermediary step reduces the harmful effects of background noise while preserving the useful speech content, allowing the model to learn from real-world noisy conditions without being overwhelmed by the noise.
Data Source
AI summary
A method of training a speech model includes receiving, at a voice-enabled device, a fixed set of training utterances where each training utterance in the fixed set of training utterances includes a transcription paired with a speech representation of the corresponding training utterance. The method also includes sampling noisy audio data from an environment of the voice-enabled device. For each training utterance in the fixed set of training utterances, the method further includes augmenting, using the noisy audio data sampled from the environment of the voice-enabled device, the speech representation of the corresponding training utterance to generate noisy audio samples and pairing each of the noisy audio samples with the corresponding transcription of the corresponding training utterance. The method additionally includes training a speech model on the noisy audio samples generated for each speech representation in the fixed set of training utterances.