Voice Interaction Satisfaction Evaluation Using CRF Feature Extraction
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Solution Overview
Problem
Intelligent voice devices face challenges in accurately evaluating user satisfaction with voice interaction, which affects their performance and user experience, as existing methods lack comprehensive and efficient evaluation mechanisms.
Innovation Solution
A method and apparatus for generating and using a model to evaluate satisfaction with voice interaction by acquiring and processing sample interaction data, extracting features, and training a Conditional Random Field (CRF) model to assess user and device features over time, enabling accurate satisfaction evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive feature extraction and CRF model training are implemented for voice interaction evaluation, then measurement precision of user satisfaction is improved, but device complexity increases
Solution Approach 1:
The evaluation system is segmented into distinct functional modules: feature extraction module that processes interaction data into specific features, CRF model training module that learns from labeled data, and satisfaction evaluation module that outputs results. This segmentation allows each module to be optimized independently while maintaining overall system precision.
Solution Approach 2:
The CRF model serves as an intermediary between raw interaction data and satisfaction evaluation results. It acts as a trained mediator that has learned the complex relationships between various interaction features and user satisfaction, thereby improving measurement precision without requiring direct complex analysis of all raw data.
2Measurement precision
If multiple dimensions of user and device features are considered in evaluation, then measurement precision is improved, but loss of time in data processing increases
Solution Approach 1:
Feature extraction is performed as a preliminary action before full evaluation. The system pre-identifies and extracts relevant features from interaction data, organizing them into structured formats. This preliminary processing reduces the complexity of subsequent analysis and enables faster comprehensive evaluation when multiple dimensions are considered.
Solution Approach 2:
The CRF model is trained to efficiently process and weigh multiple feature parameters simultaneously. By changing the parameter representation to a probabilistic framework, the system can consider multiple dimensions of user and device features in parallel, improving comprehensiveness without linearly increasing processing time.
Data Source
AI summary
A method for evaluating satisfaction with voice interaction, a device, and a storage medium are provided, which are related to a technical field of artificial intelligence, in particular, to fields of natural language processing, knowledge graph and deep learning, and can be applied to user intention understanding. The specific implementation includes: acquiring sample interaction data of a plurality of rounds of sample voice interaction behaviors; performing feature extractions on respective sample interaction data, to obtain a sample interaction feature sequence; acquiring satisfaction marks corresponding to the respective sample interaction data, to obtain a satisfaction mark sequence; and training an initial model by using a plurality of sets of sample interaction feature sequences and of satisfaction mark sequences, to obtain the model for evaluating satisfaction.