Unified Speech Processing Network for Low-Latency Dialogue
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Solution Overview
Problem
Existing speech processing systems are unreliable and slow due to the use of multiple specialized neural networks that handle speech processing tasks separately, leading to reduced accuracy and increased latency.
Innovation Solution
A unified neural network is employed to merge multiple speech processing tasks, such as ASR, NLU, NLG, and TTS, into a single network to improve accuracy and reduce latency by treating tasks as a unified whole.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple specialized neural networks are used for speech processing tasks, then task-specific performance can be optimized, but system latency increases and processing speed decreases
Solution Approach 1:
The patent combines multiple specialized neural networks (ASR, NLU, NLG, TTS) into a single unified neural network. This unified network processes speech inputs and generates speech outputs through integrated processing, eliminating the sequential execution delays between separate networks while maintaining the functional capabilities of each specialized component through shared layers and modular architecture.
2Reliability
If multiple specialized neural networks are used for speech processing tasks, then each task can be handled with dedicated expertise, but device complexity increases
Solution Approach 1:
The unified neural network is designed to perform multiple speech processing functions (automatic speech recognition, natural language understanding, natural language generation, and text-to-speech synthesis) within a single multi-functional architecture. The network uses shared embedding layers and interconnected processing units that can adapt to different tasks, reducing the need for separate specialized networks while maintaining task-specific performance through conditional processing paths and task-aware attention mechanisms.
Data Source
AI summary
A method and speech processing system for communicating with a user is provided. A speech signal may be received. The received speech signal may be processed by a first unified neural network to extract one or more of intents and entities. The one or more of intents and entities may be analyzed to generate a dialogue response. A second unified neural network may generate a speech output corresponding to the dialogue response for the user. In another example, a single unified neural network may process the received speech signal to extract one or more of intents and entities. The one or more of intents and entities may be analyzed, by the single unified neural network, to generate a dialogue response. The single unified neural network may generate a speech output corresponding to the dialogue response for the user.