Neural Network Turn-Switch Prediction in Spoken Dialogues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition systems face challenges in predicting turn-switches and speaker intentions in conversations, leading to inefficiencies in managing dialogue and determining the next speaker, especially in dyadic spoken dialogues.
Innovation Solution
A system employing a neural network that concurrently processes acoustic cues and dialogue acts to predict turn-switches and speaker intentions, using Multi-task neural networks and Long Short-Term Memory (LSTM) networks to analyze sequences of acoustic frames and optimize the prediction of turn-switches and dialogue acts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition systems are used to predict turn-switches and speaker intentions, then the system structure remains simple, but the accuracy and performance of detecting turn-switches and speaker intentions deteriorates
Solution Approach 1:
The system segments the speech processing task into multiple independent components: acoustic feature extraction, transition type classification, dialogue act classification, and turn-switch prediction. Each component is processed separately by specialized neural network modules, allowing for improved accuracy in each sub-task while maintaining modular system architecture that manages complexity.
Solution Approach 2:
The neural network system is designed with multi-functionality to simultaneously perform multiple tasks: acoustic cue processing, transition type identification, dialogue act recognition, and turn-switch prediction. This universal approach allows a single integrated system to handle diverse speech analysis functions, improving overall detection accuracy without requiring separate specialized systems for each function.
2Measurement precision
If a neural network concurrently processes acoustic cues and dialogue acts to predict turn-switches, then the detection accuracy improves, but the processing time and computational complexity increases
Solution Approach 1:
The system performs preliminary processing of acoustic cues and dialogue acts before final turn-switch prediction. Acoustic features are extracted and classified into transition types and dialogue acts in advance, creating pre-processed representations that can be quickly combined for turn-switch prediction. This preliminary action reduces the computational burden during real-time prediction, mitigating processing time increases.
Solution Approach 2:
The neural network processes acoustic cues and dialogue acts in a continuous manner rather than discrete separate steps. The system maintains continuous processing streams for acoustic feature extraction, transition type classification, and dialogue act recognition, allowing parallel computation that minimizes idle time and reduces overall processing duration while maintaining high accuracy.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products that can facilitate predicting a source of a subsequent spoken dialogue are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a speech receiving component that can receive a spoken dialogue from a first entity. The computer executable components can further comprise a speech processing component that can employ a network that can concurrently process a transition type and a dialogue act of the spoken dialogue to predict a source of a subsequent spoken dialogue.