Speech Dialogue Processing Using Multi-Vector Embedding Models
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Solution Overview
Problem
Current speech recognition technologies face challenges in maintaining accuracy in complex application scenarios, making it difficult to guarantee correct understanding of user intentions in various fields such as home automation and smart customer service.
Innovation Solution
A method and system for processing speech dialogue using a computing device that involves obtaining text, phonetic symbol, and role vector representations through embedding models, followed by inputting these representations into a trained speech dialogue coding model to determine a summary, which is then classified to improve semantic understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition technology is used, then the system is simple and easy to implement, but the accuracy of semantic understanding deteriorates in complex application scenarios
Solution Approach 1:
The patent segments the speech processing task into multiple specialized components: speech recognition module, text embedding model, phonetic symbol embedding model, role embedding model, speech dialogue coding model, and classification model. Each component handles a specific aspect of the processing pipeline, allowing for improved accuracy through specialized processing while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent creates a composite representation by combining outputs from multiple embedding models (text embedding, phonetic symbol embedding, role embedding) into a unified vector representation. This composite approach integrates diverse linguistic and contextual information to enhance semantic understanding accuracy beyond what any single model could achieve alone.
2Measurement precision
If multiple embedding models and coding models are used, then the accuracy of speech dialogue processing is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary embedding transformations on speech dialogue data using text embedding, phonetic symbol embedding, and role embedding models before the main coding and classification stages. By pre-processing and transforming the data into standardized vector representations in advance, the system optimizes subsequent processing efficiency while maintaining high accuracy through comprehensive feature extraction.
3Measurement precision
If comprehensive vector representations including text, phonetic symbols, and roles are used, then the semantic understanding accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent employs a universal vector representation framework where multiple embedding models (text, phonetic symbol, role) output standardized vector formats that can be seamlessly integrated. This multi-functional approach allows the same coding and classification models to process diverse linguistic information types uniformly, improving semantic understanding while managing processing complexity through standardized interfaces.
Data Source
AI summary
The present disclosure is related to systems and methods for processing speech dialogue. The method includes obtaining target speech dialogue data. The method includes obtaining a text vector representation sequence, a phonetic symbol vector representation sequence, and a role vector representation sequence by performing a vector transformation on the target speech dialogue data based on a text embedding model, a phonetic symbol embedding model, and a role embedding model, respectively. The method includes determining a representation vector corresponding to the target speech dialogue data by inputting the text vector representation sequence, the phonetic symbol vector representation sequence, and the role vector representation sequence into a trained speech dialogue coding model. The method includes determining a summary of the target speech dialogue data by inputting the representation vector into a classification model.