ASR-NLU Integration for Speech Recognition Latency
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Solution Overview
Problem
Existing speech-based conversation interaction frameworks, relying on separate Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) models, face errors that are amplified and incur high computational costs, leading to delayed recognition processes.
Innovation Solution
A method that integrates ASR and NLU models by converting speech into text, obtaining intention and slot information, and using a conversation state machine to generate interaction information, thereby reducing processing time and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate ASR and NLU models are used for speech recognition, then the system can perform semantic analysis, but the recognition process is delayed and computational costs increase
Solution Approach 1:
The patent combines ASR and NLU models into an integrated speech recognition system. The NLU model directly processes acoustic features from the ASR model without requiring separate text output, enabling end-to-end speech-to-intention recognition and reducing processing delays while maintaining accuracy.
Solution Approach 2:
The system performs preliminary semantic analysis by integrating the NLU model to process ASR outputs in real-time. This preliminary processing of semantic information during the recognition pipeline reduces subsequent computational overhead and accelerates the overall recognition process.
2Measurement precision
If a large model is used for speech recognition, then recognition accuracy is improved, but calculation amount increases and system cost rises
Solution Approach 1:
By merging ASR and NLU models into a unified architecture, the system eliminates redundant processing steps. The integrated model shares computational resources and processing pipelines, reducing overall calculation requirements while maintaining the accuracy benefits of large models.
Solution Approach 2:
The patent extracts and processes only the essential semantic information needed for speech recognition through the integrated NLU model. This selective extraction of critical features reduces unnecessary computational overhead while preserving recognition accuracy.
3Measurement precision
If separate ASR and NLU models are used, then comprehensive semantic analysis is achieved, but error propagation occurs and system complexity increases
Solution Approach 1:
The integration of ASR and NLU models into a unified system reduces the number of independent components and interfaces. This consolidation simplifies the system architecture while maintaining comprehensive semantic analysis capabilities, reducing error propagation points between separate models.
Data Source
AI summary
A conversation interaction method and apparatus, and a computer-readable storage medium are provided. The method includes: converting a speech to be recognized into a first text; inputting the first text into a semantic analysis model, to obtain intention information and slot information of the first text; and inputting the intention information and the slot information of the first text into a conversation state machine, to obtain interaction information corresponding to the first text. By using a semantic analysis model, intention information and slot information of a first text are obtained directly from the first text. The process in the existing technology, where a semantic analysis model needs to be used immediately after a language model, is avoided, thereby shortening processing time and making it possible to respond faster to a user. Further, by using the above scheme, calculation complexity and the cost of a whole system are reduced.