LSTM Speech Recognition Model Layered Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition methods based on the LSTM model face challenges with high operation volume and slow operation speed, leading to inefficiencies in speech recognition processes.
Innovation Solution
The proposed speech recognition method employs an LSTM model with multiple processing layers, where each processing unit determines its output through two single loops based on input and historical state data sets, reducing the number of matrix multiplication operations and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional LSTM model operations are used for speech recognition, then recognition accuracy can be maintained, but operation volume is high and operation speed is slow
Solution Approach 1:
The patent segments the LSTM model into multiple processing layers (e.g., three layers as shown in FIG. 1), with each layer containing multiple processing units that operate independently. This segmentation allows parallel processing of speech data across different layers, reducing overall processing time while maintaining recognition accuracy. Each processing unit handles specific computational tasks, dividing the large operation volume into smaller, manageable segments that can be executed more efficiently.
2Productivity
If traditional LSTM model operations are used for speech recognition, then recognition accuracy can be maintained, but operation volume is high
Solution Approach 1:
The patent extracts and removes redundant computational operations from the traditional LSTM model. By analyzing the computational graph, the invention identifies and eliminates unnecessary matrix multiplications and operations that do not contribute to the final recognition result. This extraction process reduces the overall operation volume while preserving the essential computational pathways needed for accurate speech recognition.
Solution Approach 2:
The patent transforms the computational structure from a traditional sequential LSTM operation into a multi-layered architecture with different dimensional processing. Each processing layer operates in a different computational dimension, allowing the model to process information more efficiently. This dimensional transformation reduces the computational complexity and operation volume by distributing calculations across multiple layers rather than concentrating them in a single sequential process.
Data Source
AI summary
Disclosed in embodiments of the present disclosure are a speech recognition method and apparatus, an electronic device, and a storage medium. The method comprises: inputting a speech segment to be recognized into a long short-term memory (LSTM) model; and processing the speech segment by means of the LSTM model to obtain a speech recognition result, wherein the LSTM model comprises at least one processing layer, each processing layer comprises a plurality of processing units, and each processing unit determines the output amount at a target moment of a corresponding unit by means of two single loops on the basis of an input data set of the corresponding unit and a historical state data set before the target moment.