Context-Aware Speech Recognition via Domain Categorization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional spoken language understanding (SLU) systems face challenges in accurately recognizing and responding to spoken queries due to the lack of domain-specific context, leading to inefficiencies in natural language processing.

Innovation Solution

The system provides input context for statistical dialog managers by categorizing spoken queries based on context clues from web browsers, such as domain names and input field names, and uses this context to convert spoken queries into text and generate responses.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If wide-ranging statistical models are used for speech recognition, then the system can handle diverse spoken queries, but the recognition accuracy decreases compared to tightly focused domain-specific models

Engineering Contradiction:

Improvecapability to handle diverse spoken queriesVSAvoidspeech recognition accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system segments the speech recognition task by dividing it into multiple stages: initial speech recognition using wide-ranging models, followed by categorization of the recognized text into domain-specific categories, and finally refinement using tightly focused statistical models appropriate for each category. This segmentation allows the system to handle diverse queries while maintaining high accuracy for each specific domain.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary categorization of the spoken query text into domain-specific categories before applying the final speech recognition. By first determining the domain context (e.g., weather, sports, news) and then selecting the appropriate specialized model, the system prepares the optimal recognition pathway in advance, improving overall accuracy while maintaining versatility.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If domain-specific rule-based grammars are used for speech recognition, then the recognition accuracy improves for specific domains, but the system cannot handle queries outside those domains

Engineering Contradiction:

Improvespeech recognition accuracyVSAvoidcapability to handle diverse spoken queries

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system implements a universal architecture that can handle multiple domains by combining wide-ranging statistical models with dynamic model selection. The categorization module enables the system to adaptively switch between different domain-specific models based on the query content, making a single system capable of serving multiple functions across diverse domains while maintaining high accuracy in each.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system dynamically selects and switches between different statistical models based on the categorized domain of the input query. Rather than using a fixed rule-based grammar, the system adapts its recognition approach in real-time by choosing the most appropriate model for the detected domain, enabling both versatility and precision.

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If context information from web browsers is integrated into speech recognition, then the natural language understanding accuracy improves, but the system complexity increases

Engineering Contradiction:

Improvenatural language understanding accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces a text categorization module as an intermediary between speech recognition and the statistical dialog manager. This mediator analyzes the recognized text, extracts domain-specific context clues, and selects the appropriate specialized model, thereby improving understanding accuracy while managing system complexity through modular design.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback by using the categorized domain information to refine and guide the speech recognition process. The context clues extracted from the text feed back into the model selection process, creating a closed-loop system that continuously improves accuracy by adapting to the detected domain while maintaining a manageable architectural structure.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12217750B2Using multiple modality input to feedback context for natural language understanding

Publication Date: 2025.02.04 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Input context for a statistical dialog manager may be provided. Upon receiving a spoken query from a user, the query may be categorized according to at least one context clue. The spoken query may then be converted to text according to a statistical dialog manager associated with the category of the query and a response to the spoken query may be provided to the user.