Multilevel Speech Recognition Using Keyword Extraction
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Solution Overview
Problem
Current speech recognition technologies face low accuracy and usability in processing user speech commands, limiting the effectiveness of speech interfaces in devices like smartphones and wearable devices.
Innovation Solution
A multilevel speech recognition method that extracts keywords from user commands, provides candidate application groups, and processes subsequent commands using category and application-specific speech recognition models, which are updated based on user patterns and characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a speech interface processes user commands directly without intermediate steps, then the processing speed is fast, but the recognition accuracy is low
Solution Approach 1:
The speech recognition process is divided into multiple stages: first speech command recognition, keyword extraction, candidate application group identification, and second speech command processing. Each stage uses specialized models (category speech recognition model with acoustic and linguistic models, application speech recognition model) to handle specific tasks, improving overall accuracy while managing complexity through modular processing
Solution Approach 2:
The system performs preliminary keyword extraction and candidate application group identification before processing the complete speech command. By pre-processing the input to identify relevant keywords and filter candidate applications, the system narrows down the recognition scope, thereby improving accuracy in the subsequent detailed processing stage
2Measurement precision
If the speech recognition model covers all possible applications and services, then the versatility is high, but the recognition accuracy decreases
Solution Approach 1:
The system segments the vast application space into category-level groups and application-level specific models. Instead of training one model to recognize all applications, it creates hierarchical models: category speech recognition models for broad categories and application speech recognition models for specific applications within those categories, maintaining both accuracy and versatility
Solution Approach 2:
Different speech recognition models are applied at different levels of the hierarchy. Category-level models use acoustic and linguistic models optimized for category-specific vocabulary, while application-level models use models tuned to specific application terminology. This localized optimization ensures high accuracy for each specific domain while collectively covering diverse applications
3Ease of operation
If the speech interface uses a single recognition model for all commands, then the system complexity is low, but the usability is low
Solution Approach 1:
The system dynamically adapts its recognition approach based on the input. It uses category speech recognition models for initial keyword extraction and candidate identification, then transitions to application-specific speech recognition models for detailed command processing. The system also updates models based on user patterns and characteristics, making the complexity adaptive rather than static
Data Source
AI summary
A multilevel speech recognition method and an apparatus performing the method are disclosed. The method includes receiving a first speech command from a user through a speech interface, and extracting a keyword from the first speech command. The method also includes providing a candidate application group of a category providing a service associated with the keyword, and processing a second speech command from the user associated with an application selected from the candidate application group.