Dynamic Name Weighting for Spoken Dialog Recognition
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Solution Overview
Problem
Current spoken language processing systems face challenges in accurately recognizing proper names and their variations, especially in large name lists, due to their static focus and inability to handle dynamic name references in stressful and conversational contexts, leading to reduced accuracy and effectiveness.
Innovation Solution
A name recognition process that assigns weighting values to names based on context and usage, incorporating a name model generator module to prioritize recently mentioned names and accommodate partial names, thereby improving speech recognition and language understanding by narrowing the focus to contextually relevant names.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If present recognition methods focus strictly on static name lists, then the system structure is simple, but the speech recognition accuracy deteriorates due to confusability in large name lists and inability to handle dynamic name references
Solution Approach 1:
The patent applies dynamics by transitioning from static name lists to dynamic name models that adapt to conversational context. The name model generator continuously updates name probabilities based on dialog history, making the recognition system dynamic rather than static. This allows the system to handle name variations and partial names effectively while maintaining accuracy.
Solution Approach 2:
The patent implements preliminary action through the name model generator that pre-computes and ranks name probabilities before actual recognition occurs. By generating and weighting names in advance based on context and usage patterns, the system prepares a prioritized name list that reduces confusability during real-time speech recognition.
2Adaptability or versatility
If the system accommodates all name variations and partial names, then the adaptability improves, but the recognition accuracy deteriorates due to increased confusability in large name lists
Solution Approach 1:
The patent applies local quality by assigning different weights and probabilities to different names based on their contextual relevance. Rather than treating all names equally, the system locally adjusts name probabilities based on dialog history, usage patterns, and contextual cues. This allows the system to accommodate name variations while maintaining accuracy by focusing computational resources on the most likely names.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting name probabilities and weights based on conversational context. The name model generator modifies name parameters (probabilities, rankings) in real-time based on dialog history and usage patterns, allowing the system to adapt to different speaking styles and contexts while maintaining recognition accuracy.
3Quantity of substance
If the system uses full proper names from large databases, then the completeness of name information is high, but the speech recognition accuracy deteriorates due to confusability and computational complexity
Solution Approach 1:
The patent applies extraction by selectively extracting and prioritizing the most relevant names from large databases based on contextual information. The name model generator extracts names that are most likely to be mentioned given the dialog context, usage patterns, and name frequency data. This creates a reduced but highly relevant name list that maintains accuracy while reducing computational complexity.
Data Source
AI summary
Embodiments of a name recognition process for use in dialog systems are described. In one embodiment, the name recognition process assigns weighting values to names used in a dialog based on the usage of these names. This process takes advantage of the general tendency of people to speak names, either full or partial, only after they have heard or read these names. Name input is taken in several different forms, including a static background database that contains all possible names, a background database that contains commonly used names (such as common trademarks or references), a database that contains names from a user model, and a dynamic database that constantly takes the names just mentioned. The names are then appended with proper weighting values. A high weight is given to names that have been mentioned recently, a lower weight is given to common names, and a lowest weight is given to names for the ones that have never been used or mentioned.