Goal Segmentation in Speech Dialogs Using Predictive Models
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Solution Overview
Problem
Spoken dialog systems face challenges in accurately identifying and fulfilling user goals within speech dialogs, often requiring multiple turns and relying on human analysis for performance evaluation, which is time-consuming and inefficient.
Innovation Solution
A goal-oriented speech dialog system uses a predictive model created from training data to automate the identification of goal segments by analyzing dialog turns, employing machine learning techniques like linear chain conditional random fields to classify dialog turns and determine goal boundaries, reducing the need for human analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysis is used to evaluate speech dialog performance, then accuracy in identifying user goals is improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The system performs self-evaluation by automatically analyzing its own speech dialogs using the trained predictive model. The speech dialog system processes its recorded interactions, identifies goal segments, and evaluates performance without requiring external human analysis, thereby resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The system performs preliminary training of the predictive model using manually annotated data before deployment. This preliminary human analysis creates a trained model that can then automatically perform goal segment identification and performance evaluation, transferring the accuracy benefits of human analysis to an automated process that operates quickly at scale.
2Measurement precision
If manual analysis is used to identify goal segments, then precision in goal boundary identification is improved, but productivity and scalability deteriorate
Solution Approach 1:
The system automatically analyzes its own speech dialogs to identify goal segments using the trained predictive model. This self-service capability enables the system to process large volumes of dialog data without manual intervention, dramatically improving productivity while maintaining the precision learned during training.
Solution Approach 2:
The system creates a predictive model that copies the goal boundary identification expertise of human annotators. By training on manually annotated data, the model captures human precision in identifying goal segments and replicates this capability automatically across all speech dialogs, enabling both high precision and high productivity.
3Productivity
If automated methods are used to identify goal segments, then productivity is improved, but measurement precision and reliability deteriorate
Solution Approach 1:
The system performs preliminary training using manually annotated speech dialogs to establish ground truth for goal segment boundaries. This preliminary human analysis creates a trained predictive model that learns accurate goal boundary identification, ensuring measurement precision is established before automated processing begins.
Solution Approach 2:
The system uses the trained predictive model to automatically analyze speech dialogs and identify goal segments. The model processes each dialog turn, determines whether it marks a goal segment boundary, and outputs structured results. This feedback loop allows the system to maintain consistent, reliable measurements across large datasets while preserving the precision learned during training.
Data Source
AI summary
A speech-based system is configured to interact with a user through speech to determine intents and goals of the user. The system may analyze multiple dialog turns in order to determine and fully define a goal that the user is trying to express. Each dialog turn comprises a user utterance. Each dialog turn may also comprise a system speech response. In order to evaluate the performance of the system, logged data is analyzed to identify goal segments within the logged data, where a goal segment is a sequence of dialog turns that relate to a corresponding user goal. A subset of the dialog turns is annotated manually to delineate goal segments. A predictive model is then constructed based on the manually annotated goal segments. The predictive model is then used to identify goal segments formed by additional dialog turns.