RNN Phoneme Transduction With Partial Conditioning for Real-Time Speech
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Solution Overview
Problem
Existing sequence transduction systems require the entire input sequence to be processed before generating outputs, limiting their ability to perform online tasks such as real-time speech recognition and translation.
Innovation Solution
A recurrent neural network (RNN) system that uses an encoder RNN to generate feature representations for input blocks and a transducer RNN to select outputs based on partial input and output sequences, allowing for online sequence transduction by generating outputs before the entire input sequence is observed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire input sequence is processed 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 a fixed number of time steps. The encoder RNN processes each block independently to generate corresponding output blocks, enabling parallel processing of sequence segments and reducing overall processing time while maintaining accuracy through block-wise attention mechanisms.
Solution Approach 2:
The encoder RNN pre-processes the entire input sequence to generate feature representations before the decoder RNN generates outputs. This preliminary encoding allows the decoder to access comprehensive input information without waiting for sequential processing, improving both speed and accuracy.
2Loss of information
If the entire input sequence is processed before generating outputs, then the completeness of information used for output selection is improved, but the processing time increases
Solution Approach 1:
The input sequence is segmented into multiple blocks that are processed in parallel by the encoder RNN. Each block generates feature representations that are stored and made available to the decoder, allowing the system to utilize complete information across all blocks without sequential processing delays.
Solution Approach 2:
The encoder RNN acts as an intermediary that transforms the raw input sequence into compressed feature representations. These intermediate features capture essential information from the entire input sequence and are passed to the decoder, enabling efficient information transfer without transmitting the complete raw sequence.
3Measurement precision
If outputs are generated sequentially after complete input processing, then the accuracy of each output is improved, but the productivity of the system deteriorates
Solution Approach 1:
The output generation is segmented into multiple blocks corresponding to input blocks. The decoder RNN generates multiple output blocks in parallel by processing feature representations from different input blocks simultaneously, increasing the system's overall output rate while maintaining accuracy through block-wise processing.
Solution Approach 2:
The decoder RNN maintains continuous operation by generating outputs for multiple blocks without idle periods. While the encoder processes one block, the decoder can simultaneously process features from previously encoded blocks, ensuring continuous productive action and maximizing system throughput.
Data Source
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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.