Attention-Based RNN Speech Recognition
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Solution Overview
Problem
Conventional speech recognition systems face limitations in accurately transcribing speech utterances, particularly in handling accents, rare words, and computational efficiency, due to separate components and conditional independence assumptions.
Innovation Solution
The Listen, Attend and Spell (LAS) speech recognition system processes input acoustic sequences using a pyramid Bidirectional Long Short Term Memory (BLSTM) neural network to generate alternative representations, which are then processed by an attention-based Recurrent Neural Network (RNN) to predict substrings, allowing for joint learning of all components and next-step prediction, reducing time steps and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate components (pronunciation system, acoustic modeling system, language model) are used in speech recognition, then the system can process speech through modular stages, but the system complexity increases and accuracy deteriorates due to conditional independence assumptions
Solution Approach 1:
The patent combines separate speech recognition components (acoustic modeling, pronunciation, language modeling) into a unified neural network architecture. The encoder-decoder framework with attention mechanisms integrates these previously separate modules, allowing joint optimization and eliminating the conditional independence assumptions that limited accuracy in modular systems.
2Productivity
If conventional speech recognition systems use separate processing stages, then computational steps can be divided, but the overall computational complexity increases and processing speed decreases
Solution Approach 1:
The attention mechanism enables continuous and dynamic processing by allowing the decoder to selectively attend to relevant encoder outputs at each decoding step. This continuous attention mechanism eliminates the need for separate, discrete processing stages, reducing computational overhead while maintaining processing efficiency.
3Device complexity
If conventional systems make conditional independence assumptions, then computation can be simplified, but accuracy in handling accents and rare words deteriorates
Solution Approach 1:
The attention mechanism provides dynamic feedback by allowing the decoder to selectively attend to relevant parts of the encoder output sequence. This feedback loop enables the model to capture long-range dependencies and contextual relationships, improving accuracy for accents and rare words without requiring complex separate processing stages.
4Reliability
If more pre-training is performed in conventional systems, then model robustness can be improved, but data footprint increases and computational resources required increase
Solution Approach 1:
The encoder processes the entire input sequence and creates comprehensive contextual representations in advance, performing preliminary action on all input data. This pre-computation of contextual information at the encoder stage reduces the need for extensive additional pre-training, as the essential features are already extracted and made available to the decoder.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for speech recognition. One method includes obtaining an input acoustic sequence, the input acoustic sequence representing an utterance, and the input acoustic sequence comprising a respective acoustic feature representation at each of a first number of time steps; processing the input acoustic sequence using a first neural network to convert the input acoustic sequence into an alternative representation for the input acoustic sequence; processing the alternative representation for the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) to generate, for each position in an output sequence order, a set of substring scores that includes a respective substring score for each substring in a set of substrings; and generating a sequence of substrings that represent a transcription of the utterance.