RNN-T Encoder Customization via Audio Synthesis
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Solution Overview
Problem
Recurrent neural networks (RNNs) for speech recognition require pre-defined alignments between input and output sequences, which can be difficult to find, limiting their effectiveness in transduction tasks, and once trained, their language and vocabulary modules cannot be easily customized or manipulated after deployment.
Innovation Solution
A method and system for customizing a recurrent neural network transducer (RNN-T) by synthesizing audio data from text, updating the encoder and predictor networks using this data, and restoring the encoder to its initial condition, allowing for domain-specific customization without pre-computed alignments and enabling manipulation of language models and vocabularies post-deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RNN-T is trained with pre-defined alignments between input and output sequences, then the model can perform transduction tasks, but the alignment finding process becomes difficult and limits effectiveness
Solution Approach 1:
The patent applies preliminary action by pre-training the encoder and predictor networks separately before final transduction training. The encoder is pre-trained on acoustic features to text alignments, and the predictor is pre-trained on text to acoustic features, establishing preliminary relationships that facilitate the final transduction task without requiring complex real-time alignment finding.
Solution Approach 2:
The patent segments the transduction process into distinct components: an encoder that processes acoustic features to text, a predictor that processes text to acoustic features, and a joint training mechanism. This segmentation allows each component to be optimized independently and combined effectively, avoiding the need for a single monolithic alignment finding process.
2Adaptability or versatility
If RNN-T is trained with fixed parameters, then the model structure is simplified, but the language and vocabulary modules cannot be customized after deployment
Solution Approach 1:
The patent implements dynamics by enabling post-deployment customization of the language model and vocabulary modules while maintaining the fixed encoder structure. The predictor network can be dynamically updated with domain-specific language models and vocabularies without requiring retraining of the entire system, providing adaptability without full model retraining complexity.
Solution Approach 2:
The patent extracts the customization capability from the entire model by separating the encoder (which remains fixed) from the predictor and language model components (which can be customized). This extraction allows domain-specific adaptations to be applied to the language and vocabulary modules independently, maintaining model flexibility while reducing overall complexity.
3Adaptability or versatility
If RNN-T processes variable length sequences, then the model handles diverse input lengths, but the relationship between input positions and hidden layers becomes complex
Solution Approach 1:
The patent applies universality by designing the encoder and predictor networks with architectures that can handle multiple sequence lengths through the same structural framework. The recurrent nature of the networks allows them to process variable length sequences by simply adjusting the number of time steps, without requiring different architectural configurations, thus maintaining simplicity while achieving versatility.
Data Source
AI summary
A computer-implemented method for customizing a recurrent neural network transducer (RNN-T) is provided. The computer implemented method includes synthesizing first domain audio data from first domain text data, and feeding the synthesized first domain audio data into a trained encoder of the recurrent neural network transducer (RNN-T) having an initial condition, wherein the encoder is updated using the synthesized first domain audio data and the first domain text data. The computer implemented method further includes synthesizing second domain audio data from second domain text data, and feeding the synthesized second domain audio data into the updated encoder of the recurrent neural network transducer (RNN-T), wherein the prediction network is updated using the synthesized second domain audio data and the second domain text data. The computer implemented method further includes restoring the updated encoder to the initial condition.