Joint Decoder Speech Processing Adaptability
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Solution Overview
Problem
Spoken language understanding (SLU) systems face challenges in configuring their components due to a lack of or limited availability of training data, particularly in new domains, intents, and entities, which hampers their ability to accurately process and understand spoken inputs.
Innovation Solution
The SLU system is configured using a combination of ASR, NLU, and SLU training data, with pre-training on available data and fine-tuning using SLU training data, incorporating an audio encoder, text encoder, and joint decoder to perform multiple processing tasks such as audio-to-text, text-to-NLU, and audio-to-NLU, enabling it to adapt to new domains and entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-training and fine-tuning phases are used with limited SLU training data, then the system can adapt to new domains and intents, but the system complexity increases due to multiple processing stages
Solution Approach 1:
The system performs pre-training on general language data before fine-tuning on domain-specific SLU data. This preliminary action allows the model to learn general patterns first, then specialize for specific domains, reducing the amount of domain-specific training data needed while maintaining adaptability.
Solution Approach 2:
The training process is segmented into distinct phases: pre-training on general data, then fine-tuning on SLU data. This segmentation allows each phase to focus on specific learning objectives, improving overall system adaptability while making the complex training process more manageable and efficient.
2Adaptability or versatility
If multiple processing tasks are performed (audio-to-text, text-to-NLU, audio-to-NLU), then the system versatility improves, but the processing time increases
Solution Approach 1:
The system merges multiple processing tasks (audio-to-text, text-to-NLU, audio-to-NLU) into a unified neural network architecture. This allows the model to learn shared representations across tasks and perform them in an integrated manner, improving versatility while reducing redundant processing time.
Solution Approach 2:
The neural network is designed with universal components that can handle multiple processing tasks. The audio encoder, text encoder, and joint decoder work together to perform different tasks depending on the input and desired output, enabling the system to be versatile without requiring separate dedicated models for each task.
3Device complexity
If the system uses a joint decoder for multiple tasks, then the device complexity is reduced, but the measurement precision of individual tasks may deteriorate
Solution Approach 1:
While using a joint decoder for multiple tasks, the system applies task-specific processing and optimization at local levels. Each task (audio-to-text, text-to-NLU, audio-to-NLU) receives tailored processing within the unified architecture, ensuring high precision for individual tasks while maintaining overall system simplicity.
Data Source
AI summary
Techniques for performing spoken language understanding (SLU) processing are described. An SLU component may include an audio encoder configured to perform an audio-to-text processing task and an audio-to-NLU processing task. The SLU component may also include a joint decoder configured to perform the audio-to-text processing task, the audio-to-NLU processing task and a text-to-NLU processing task. Input audio data, representing a spoken input, is processed by the audio encoder and the joint decoder to determine NLU data corresponding to the spoken input.