Multi-Stream RNN-T Joint Decoding for Shared Sequence Learning
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Solution Overview
Problem
Conventional recurrent neural network transducers (RNN-Ts) require separate models for generating multiple output sequences, leading to inefficiencies in computing resources and longer training times due to the lack of joint learning of dependencies between sequences.
Innovation Solution
A multi-stream recurrent neural network transducer (MS RNN-T) model processes multiple input sequences using a single unified model to jointly generate multiple output sequences, leveraging a joint network to condition outputs on previously generated sequences and conserve computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate RNN-T models are used for each output sequence, then each model can be optimized independently, but computing resources increase and training time extends
Solution Approach 1:
The patent merges multiple separate RNN-T models into a single unified model that processes multiple input sequences and generates multiple output sequences simultaneously. This consolidation reduces the total number of models from multiple separate instances to one integrated system, thereby reducing computing resource requirements and training time while maintaining the ability to handle multiple sequence classification tasks.
Solution Approach 2:
The unified RNN-T model is designed with multi-functionality to handle diverse sequence classification tasks within a single architecture. It can process different types of input sequences (e.g., audio, text) and generate different types of output sequences (e.g., phoneme sequences, grapheme sequences) using the same model structure, eliminating the need for separate specialized models for each task.
2Reliability
If separate RNN-T models are used for each output sequence, then each model can be trained independently, but training time increases due to lack of joint learning
Solution Approach 1:
The patent combines multiple independent training processes into a single joint training process. The unified model learns from multiple input-output sequence pairs simultaneously, enabling joint learning of dependencies between different sequences. This approach reduces total training time compared to sequentially or independently training multiple separate models, while improving performance through shared knowledge across tasks.
Solution Approach 2:
The joint training process maintains continuous learning across all sequence classification tasks simultaneously. Instead of alternating between training separate models or training them in isolation, the unified model continuously learns from all available training data across different sequence types in an integrated manner, maximizing the efficiency of each training iteration.
3Productivity
If a single unified model is used to process multiple input sequences, then computing resources are conserved, but the model complexity increases
Solution Approach 1:
The unified model employs a universal architecture that can handle multiple input sequence types and generate multiple output sequence types through a single set of processing components. This multi-functional design achieves resource efficiency by consolidating computing resources into one model while managing complexity through a standardized architecture that processes different sequence types using the same mechanisms.
Solution Approach 2:
The model architecture is segmented into distinct functional components (encoders for different input streams, decoders for different output streams, and attention mechanisms) that can be independently configured and optimized. This segmentation allows the complex unified model to be built from modular components, making the complexity manageable while maintaining resource efficiency through shared underlying structures.
Data Source
AI summary
Techniques are disclosed that enable generating jointly probable output by processing input using a multi-stream recurrent neural network transducer (MS RNN-T) model. Various implementations include generating a first output sequence and a second output sequence by processing a single input sequence using the MS RNN-T, where the first output sequence is jointly probable with the second output sequence. Additional or alternative techniques are disclosed that enable generating output by processing multiple input sequences using the MS RNN-T. Various implementations include processing a first input sequence and a second input sequence using the MS RNN-T to generate output. In some implementations, the MS RNN-T can be used to process two or more input sequences to generate two or more jointly probable output sequences.