Partial-Conditioned Sequence Transduction for Real-Time Speech Output
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Solution Overview
Problem
Existing systems for generating output sequences, such as machine translation and pronunciation systems, face challenges in processing input sequences in real-time, as they often require the entire input sequence to be observed before generating outputs, which can be inefficient for online tasks like real-time transcription.
Innovation Solution
The system employs a combination of encoder and transducer recurrent neural networks (RNNs) to generate output sequences by processing input sequences in blocks, allowing for partial conditioning on observed inputs and generated outputs, enabling the selection of outputs before the entire input sequence is processed, using a transducer RNN to generate outputs based on feature representations and preceding outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire input sequence is observed before generating outputs, then the accuracy of output generation is improved, but the real-time processing capability deteriorates
Solution Approach 1:
The input sequence is divided into blocks of fixed number of time steps. The system processes each block independently using encoder RNN to generate feature representations, and then uses transducer RNN to generate corresponding output blocks. This segmentation allows the system to generate outputs for observed blocks without waiting for the entire input sequence, thus reducing real-time processing delay while maintaining acceptable accuracy through block-level completeness.
Solution Approach 2:
The system generates output blocks in advance as soon as their corresponding input blocks are observed and processed, rather than waiting for the complete input sequence. The transducer RNN generates outputs conditioned on available feature representations and preceding outputs, performing preliminary action that improves real-time responsiveness while the accuracy is maintained through progressive refinement as more input blocks become available.
2Productivity
If outputs are generated before the entire input sequence is processed, then the real-time processing capability is improved, but the accuracy of output generation deteriorates
Solution Approach 1:
The transducer RNN conditions output generation not only on feature representations from encoder RNN but also on preceding outputs in the target sequence. This feedback mechanism allows the system to maintain accuracy by continuously refining outputs based on both input context and previously generated outputs, even when processing in blocks before the entire input sequence is observed.
Solution Approach 2:
The system performs partial action by generating outputs for only the observed input blocks rather than waiting for the complete sequence. This partial action improves real-time processing speed, and the accuracy is compensated by using both encoder features and transducer feedback from preceding outputs to condition the generation process.
3Productivity
If the system processes input sequences in blocks, then the real-time generation capability is improved, but the system complexity increases
Solution Approach 1:
The system segments the transduction task into two distinct RNN components: encoder RNN for processing input blocks and generating feature representations, and transducer RNN for generating output blocks conditioned on features and preceding outputs. This segmentation of functionality, while increasing architectural complexity, enables online transduction capability by allowing block-level processing without requiring the entire input sequence.
Data Source
AI summary
A system can be configured to perform tasks such as converting recorded speech to a sequence of phonemes that represent the speech, converting an input sequence of graphemes into a target sequence of phonemes, translating an input sequence of words in one language into a corresponding sequence of words in another language, or predicting a target sequence of words that follow an input sequence of words in a language (e.g., a language model). In a speech recognizer, the RNN system may be used to convert speech to a target sequence of phonemes in real-time so that a transcription of the speech can be generated and presented to a user, even before the user has completed uttering the entire speech input.