Modular Encoder Export for Interchangeable ASR Decoders
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Solution Overview
Problem
End-to-end speech recognition models have a tight coupling between encoders and decoders, making them non-interchangeable and limiting the development of flexible, modular architectures for speech recognition systems.
Innovation Solution
A modular encoder model generates modular encoded representations that can be processed by different downstream decoders without requiring joint training, allowing separate development and updating of encoders and decoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end-to-end speech recognition models are used with tight coupling between encoders and decoders, then the model achieves integrated training and optimization, but the system loses flexibility and interchangeability of components
Solution Approach 1:
The patent divides the end-to-end speech recognition model into separate modular components: an encoder module that generates encoded representations and a decoder module that processes them. The encoder can be trained independently using encoded representations as output, and the decoder can be trained separately using these representations as input. This segmentation enables interchangeable components while maintaining overall model performance through modular architecture.
2Reliability
If joint training of encoders and decoders is performed, then the model achieves optimized end-to-end performance, but the development flexibility and separate updating of components is limited
Solution Approach 1:
The encoder is trained in advance to generate encoded representations without requiring the decoder to be present or trained. The encoder learns to produce meaningful representations that can be used by multiple different decoders. This preliminary training action allows the encoder to be developed, updated, and optimized independently before being paired with any specific decoder, greatly easing the development process.
Solution Approach 2:
The encoder is designed to produce universal encoded representations that can serve multiple different decoder types and configurations. The encoder's output format and representation space are standardized to be compatible with various decoders, allowing a single encoder to be reused across multiple speech recognition tasks and decoder architectures, enhancing ease of development and updating.
3Adaptability or versatility
If modular encoded representations are generated for streaming and deliberation, then the system achieves flexibility in processing, but requires additional exporter and importer networks
Solution Approach 1:
The patent introduces an exporter network that transforms encoder outputs into standardized encoded representations suitable for streaming and deliberation processing. An importer network then converts these representations back to the original format for the decoder. These intermediary networks enable flexible streaming and deliberation capabilities by creating a standardized intermediate representation layer, while their modular design means they can be added without fundamentally changing the core encoder-decoder architecture.
Data Source
AI summary
A method includes obtaining a base encoder from a pre-trained model, and receiving training data comprising a sequence of acoustic frames characterizing an utterance paired with a ground-truth transcription of the utterance. At each of a plurality of output steps, the method includes: generating, by the base encoder, a first encoded representation for a corresponding acoustic frame; generating, by an exporter network configured to receive a continuous sequence of first encoded representations generated by the base encoder, a second encoded representation for a corresponding acoustic frame; generating, by an exporter decoder, a probability distribution over possible logits; and determining an exporter decoder loss based on the probability distribution over possible logits generated by the exporter decoder at the corresponding output step and the ground-truth transcription. The method also includes training the exporter network based on the exporter decoder losses while parameters of the base encoder are frozen.