Multi-Modal Neural Network for Speech-to-Text FAQ Classification
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Solution Overview
Problem
Existing FAQ systems face challenges in accurately and efficiently identifying user intentions through speech data due to spatial and situational restrictions in vehicles, leading to suboptimal performance in providing relevant information.
Innovation Solution
An electronic device equipped with a neural network model trained in an end-to-end manner, capable of using both text and speech data, performs multi-task learning with symmetric and cross-entropy losses to derive frequently-asked-questions pairs, utilizing a shared encoder, bidirectional recurrent neural network, and feed-forward neural network layers for effective FAQ classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional FAQ systems use separate processing for speech and text data, then system complexity is maintained, but classification accuracy and data efficiency deteriorate
Solution Approach 1:
The patent combines speech data and text data processing into a single unified neural network model. The model simultaneously processes both data types through shared encoder layers and unified processing pathways, enabling joint learning of patterns from multiple modalities while maintaining a single coherent system architecture rather than separate independent systems.
Solution Approach 2:
The neural network model is designed with multi-functionality to handle both speech and text data through a universal architecture. The shared encoder and unified processing layers can process different input types (speech and text) through the same computational pathways, making the system versatile and reducing overall complexity compared to maintaining separate specialized systems.
2Measurement precision
If multi-modal language model processes both speech and text simultaneously, then classification performance improves, but computational requirements increase
Solution Approach 1:
By merging speech and text processing into unified neural network pathways, the system eliminates redundant computational operations that would occur in separate processing systems. The shared encoder and common processing layers compute features for both modalities simultaneously, reducing total computational load while maintaining high classification performance.
Solution Approach 2:
The model performs preliminary processing of both speech and text data through shared encoder layers before final classification. This preliminary unified processing extracts common features and representations early in the pipeline, reducing the computational burden on subsequent layers compared to processing each modality separately through complete independent pipelines.
3Reliability
If end-to-end neural network training is implemented, then data efficiency improves, but training complexity increases
Solution Approach 1:
The end-to-end training approach merges the training processes for speech and text processing into a single unified training procedure. Gradient updates and parameter optimization occur simultaneously across all components handling both modalities, improving data efficiency by learning from combined data patterns while the unified training loop manages complexity rather than requiring separate independent training processes.
Data Source
AI summary
An electronic device includes a memory configured to store instructions and a processor electrically connected to the memory and configured to execute the instructions, in which when the instructions are executed by the processor, the processor is configured to perform a plurality of operations, in which the plurality of operations includes deriving a frequently-asked-questions (FAQ) pair from speech data based on a neural network model trained in an end-to-end manner, in which the neural network model is based on a multi-modal language model (LM) capable of using text data and speech data simultaneously, and contrastive learning is performed on the neural network model based on symmetric loss to shift speech data, which is original data, to text data, which is augmented data