Automated Spoken Dialog System for Website Data
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Solution Overview
Problem
Developing spoken dialog systems for websites is a labor-intensive and costly process that requires significant human expertise and effort, making it difficult for companies to implement cost-effective voice interfaces that can keep pace with website changes.
Innovation Solution
The WebTalk system automates the process of building spoken dialog systems by analyzing website data to extract task knowledge, constructing language models, and integrating components like Automatic Speech Recognition, Spoken Language Understanding, Dialog Management, and Text-To-Speech synthesis, eliminating the need for human intervention and enabling rapid creation of voice-enabled helpdesk and customer-care services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a handcrafted process is used to create a spoken dialog system, then the system can be customized and trained accurately, but the development time and cost increase significantly
Solution Approach 1:
The system automatically extracts domain knowledge, constructs grammars, and trains recognition models without requiring manual human intervention. The website content itself serves as the training data, eliminating the need for external experts to manually create task representation models and train the dialog system.
Solution Approach 2:
An automated processing intermediary is introduced between the website content and the spoken dialog system. This intermediary automatically parses website data, extracts relevant information, generates grammars, and trains the recognition models, replacing the manual handcrafted process while maintaining system accuracy.
2Manufacturing precision
If manual data collection and model training is performed, then the dialog system can be accurately trained, but the process becomes labor-intensive and costly
Solution Approach 1:
The system performs self-training by automatically extracting domain knowledge from website content and generating trained models without requiring manual data collection or expert intervention. The website content serves as both the knowledge source and training data, making the process self-sufficient.
Solution Approach 2:
The manual mechanical process of data collection, analysis, and model training is replaced with an automated computational system that parses website data, extracts knowledge, and trains models algorithmically, eliminating labor-intensive operations while maintaining training precision.
3Ease of operation
If VoiceXML programming is used to create a voice interface, then the interface can be developed with web-based tools, but each user interaction must be individually programmed and maintained
Solution Approach 1:
The system extracts the essential domain knowledge and interaction patterns directly from website content, separating the core functional logic from the programming implementation. This eliminates the need to manually program each user interaction while retaining web-based development advantages.
Solution Approach 2:
Instead of programming interactions from scratch using VoiceXML, the system automatically generates interaction models by copying and adapting the existing website content structure and logic, reducing programming complexity while maintaining ease of web-based development.
4Adaptability or versatility
If a spoken dialog system is manually created, then the system can be customized for specific domains, but updating the system to match website changes requires significant human effort
Solution Approach 1:
The system dynamically adapts to website changes by continuously monitoring and re-parsing website content to update its domain knowledge and grammar models automatically. This dynamic update mechanism maintains adaptability while eliminating the manual effort previously required for system updates.
Solution Approach 2:
The system implements a feedback loop where changes in website content automatically trigger re-processing and model updates. This feedback mechanism ensures the dialog system remains synchronized with website changes without requiring manual intervention, improving update speed while preserving domain customization.
Data Source
AI summary
Disclosed is a system and method for generating a spoken dialog service from website data. Spoken dialog components typically include an automatic speech recognition module, a language understanding module, a dialog management module, a language generation module and a test-to-speech module. These components are capable of being automatically trained from processed website data. A website analyzer converts a website into structured text data set and a structured task knowledge base. The website analyzer further extracts linguistic items from the website data. The dialog components are automatically trained from the structured text data set, structured task knowledge base and linguistic items.