Voice Processing Combining Similar Sentences for NLU
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Solution Overview
Problem
Existing natural language understanding systems face challenges in accurately processing multiple sentences from user voice signals without increasing computational complexity, especially when sentences have insufficient information or repeat similar intents.
Innovation Solution
An electronic apparatus and control method that utilize a processor to receive user voice signals, convert them into text, identify sentences, determine their domains, and combine similar sentences using neural network models to improve natural language understanding while reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network model is used to learn multiple sentences from user voice signals, then the accuracy of natural language understanding is improved, but the model capacity increases and processing speed decreases
Solution Approach 1:
The patent segments the user voice signal into multiple individual sentences before processing. Each sentence is then independently analyzed for domain and intent, allowing the system to handle complex multi-sentence inputs without requiring a single large neural network model, thus maintaining processing speed while improving understanding accuracy
Solution Approach 2:
The patent performs preliminary classification of each sentence by domain before conducting detailed natural language understanding. This preliminary action organizes the input sentences into domain-specific groups, enabling more efficient processing by specialized models and improving overall accuracy without significantly increasing computational load
2Measurement precision
If a neural network model is used to learn multiple sentences, then the accuracy of identifying user intent is improved, but the computation amount increases
Solution Approach 1:
The patent divides the computation task by segmenting sentences and processing them individually with domain-specific models. This segmentation allows the system to use smaller, more efficient neural networks for each domain rather than one large comprehensive model, reducing overall computation amount while maintaining intent identification accuracy
Solution Approach 2:
The patent changes the processing parameters by treating each sentence independently with domain-specific configurations rather than using a single uniform model. This parameter change enables optimized computation for each domain, reducing total computational requirements while improving intent detection accuracy through domain-specialized processing
3Device complexity
If multiple sentences are processed independently, then the processing complexity is reduced, but the accuracy of reflecting user intent deteriorates
Solution Approach 1:
The patent performs preliminary domain classification for each sentence before processing, creating an organized structure that maintains low processing complexity. This preliminary organization enables the system to capture contextual relationships between sentences within the same domain while keeping the overall processing framework simple and manageable
Data Source
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AI summary
An electronic device is disclosed. The electronic device comprises a mic; a memory for storing at least one instruction; and a processor connected to the mic and the memory so as to control the electronic device. The processor, by executing the at least one instruction, may: when a user speech signal is received through the mic, acquire text corresponding to the user speech signal; identify a plurality of sentences included in the acquired text; identify, among a plurality of domains, repective domains corresponding to each of the plurality of sentences; if the degree of similarity between a first sentence and a second sentence having the same domain, among the plurality of sentences, is equal to or higher than a threshold value, acquire, using a first neural network model, a third sentence formed by combining the first sentence and the second sentence; and carry out natural language understanding of the third sentence.