Speaker Adaptation for Attention-Based Encoder-Decoder Models
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Solution Overview
Problem
Current techniques for speaker adaptation of attention-based encoder-decoder (AED) models in automatic speech recognition (ASR) systems are limited by overfitting due to limited adaptation data, leading to degraded performance when recognizing speech from unseen speakers.
Innovation Solution
The implementation of regularization-based, adversarial, and multi-task learning methods for speaker adaptation, including Kullback-Leibler divergence regularization and adversarial speaker adaptation, to maintain similarity between deep-feature distributions and address data sparsity by training models to predict both word/subword units and character units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If re-training an SI E2E ASR system based on limited adaptation data, then speaker-dependent accuracy is improved, but the model becomes overfitted to the adaptation data
Solution Approach 1:
The patent applies partial action by selectively updating only certain model parameters during adaptation while keeping other parameters fixed. This prevents the entire model from overfitting to limited adaptation data, while still allowing speaker-specific characteristics to be captured in the updated parameters.
Solution Approach 2:
The patent changes parameters by introducing regularization terms that modify the loss function during adaptation. These parameter changes constrain the model to maintain similarity with the original SI model while adapting to the target speaker, balancing accuracy improvement with generalization preservation.
2Reliability
If constrained retraining of a partial set of model parameters is applied, then overfitting is reduced, but adaptation effectiveness is limited
Solution Approach 1:
The patent introduces regularization terms as intermediaries between the adaptation process and the model parameters. These regularization terms mediate the update process by enforcing constraints that prevent overfitting while still allowing effective adaptation, thus resolving the conflict between reliability and precision.
Solution Approach 2:
The patent implements feedback through regularization terms that continuously monitor and constrain parameter updates during adaptation. This feedback mechanism ensures that the model remains similar to the original SI model while still adapting to speaker-specific characteristics, improving both reliability and precision.
3Reliability
If a SI E2E ASR system is trained using speech frames of many different speakers, then generalization performance is improved, but performance degrades when used to recognize speech of an unseen speaker
Solution Approach 1:
The patent applies preliminary action by pre-training the model on diverse speaker data to establish strong generalization capabilities. Then, during adaptation, it performs preliminary constraints through regularization to ensure the model maintains this generalization while adapting to unseen speakers, thus improving both reliability and precision for unseen speakers.
Data Source
AI summary
Embodiments are associated with a speaker-independent attention-based encoder-decoder model to classify output tokens based on input speech frames, the speaker-independent attention-based encoder-decoder model associated with a first output distribution, and a speaker-dependent attention-based encoder-decoder model to classify output tokens based on input speech frames, the speaker-dependent attention-based encoder-decoder model associated with a second output distribution. The second attention-based encoder-decoder model is trained to classify output tokens based on input speech frames of a target speaker and simultaneously trained to maintain a similarity between the first output distribution and the second output distribution.