End-to-End Speech Recognition Model Using Attention Encoding
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Solution Overview
Problem
Hybrid speech recognition systems have low accuracy due to the poor coordination between multiple modules such as the acoustic model, pronunciation dictionary, and language model, leading to inefficient speech recognition in noisy or inconvenient environments.
Innovation Solution
A cloud-based end-to-end speech recognition system using a TL-AED model with attention encoding and sequential decoding, which simplifies the architecture and improves recognition accuracy by reducing the number of parameters and utilizing parallel computation effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hybrid speech recognition systems use multiple modules (acoustic model, pronunciation dictionary, language model), then the system can process speech recognition, but the coordination between modules is poor and recognition accuracy is low
Solution Approach 1:
The patent merges the acoustic model, pronunciation dictionary, and language model into a unified end-to-end neural network architecture. This integration eliminates the coordination problems between separate modules while maintaining the functional capabilities of speech recognition, thereby improving accuracy and reducing system complexity.
Solution Approach 2:
The end-to-end neural network is designed to perform multiple functions simultaneously: acoustic feature extraction, phoneme recognition, and language modeling within a single unified framework. This multi-functional design improves coordination and reduces the complexity associated with managing multiple specialized modules.
2Speed
If the speech recognition system processes speech in real-time, then the user experience is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the speech signal into frames and processes them sequentially through the neural network. This segmentation allows for efficient real-time processing by breaking down the continuous speech stream into manageable units that can be processed in parallel where possible, reducing overall computational complexity while maintaining real-time performance.
Solution Approach 2:
The system performs preliminary feature extraction and preprocessing of speech signals before main recognition processing. This preliminary action prepares the data in an optimized format that reduces the computational burden during real-time recognition, enabling faster processing without sacrificing accuracy.
3Reliability
If the neural network uses high numerical precision for weights and activations, then the model performance is maintained, but the computational resources and memory usage increase
Solution Approach 1:
The patent employs mixed precision arithmetic, using different numerical precisions for different parts of the neural network computation. Critical operations that require high accuracy use higher precision, while less sensitive operations use lower precision, thereby maintaining model performance while significantly reducing memory usage and computational resource consumption.
Solution Approach 2:
The system uses lower precision data types (such as float16 or bfloat16) for intermediate computations and activations that can tolerate some precision loss. These lower-precision representations consume less memory and computational resources, and the model is trained to be robust to this reduced precision, effectively using 'cheaper' computational objects where appropriate.
Data Source
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AI summary
A speech recognition method and apparatus, a device, and a storage medium, relating to the field of speech recognition. Speech recognition is performed using attention coding and time series decoding methods, and attention coding is performed on a speech feature matrix, such that the parallel computing of a GPU can be efficiently utilized. Decoding coded vectors according to the positions of the coded vectors in a coded matrix can reduce the amount of parameters, and the use of memory features of a speech recognition model to perform prediction according to time-series related coded vectors can improve speech recognition accuracy.