Voice Translation Interface Using Confidence Scoring to Cut Network Traffic
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Networked devices face challenges in processing audio-based signals due to poor audio quality, leading to excessive network traffic and inefficient use of computational resources, particularly in translating utterances between users.
Innovation Solution
A voice-based interface system that incorporates natural language processing and translation engines to generate accurate translations by using confidence scores and parallel language models, reducing redundant requests and optimizing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If audio-based signals are transmitted over the network for processing, then translation functionality is provided, but network traffic increases and bandwidth is consumed
Solution Approach 1:
The system performs preliminary speech-to-text conversion and translation scoring before network transmission. By converting audio to text locally and pre-evaluating translation quality using confidence scores, the system reduces the need to transmit low-quality audio signals that would require reprocessing, thereby reducing overall network traffic while maintaining translation functionality
Solution Approach 2:
The patent replaces direct audio signal transmission with text-based representation. Instead of transmitting raw audio waves over the network, the system converts speech to text locally and transmits only the text data, which consumes significantly less bandwidth while enabling the same translation functionality
2Measurement precision
If multiple translation strings are generated in parallel, then translation accuracy is improved, but computational resources are consumed
Solution Approach 1:
The system generates multiple translation strings in parallel but only processes a limited number based on confidence scores. By generating more translations than needed and then selecting only the top candidates based on predefined confidence thresholds, the system achieves high translation accuracy while controlling computational resource consumption through selective processing
Solution Approach 2:
The patent dynamically adjusts the number of parallel translation processes based on confidence scores. By using confidence score thresholds to determine how many translation candidates to generate and process, the system optimizes the balance between translation accuracy and computational resource usage, processing more translations only when higher accuracy is needed
3Loss of information
If audio signals with large file sizes are processed, then complete audio information is available, but processing efficiency decreases
Solution Approach 1:
The system extracts only the essential information from audio signals by converting speech to text. By extracting the semantic content (text representation) rather than processing the complete audio waveform, the system maintains all necessary information for translation while dramatically reducing file size and improving processing efficiency
Solution Approach 2:
The patent creates a text-based copy of the audio content instead of processing the original audio signal. This text copy contains all the essential information needed for translation but occupies minimal space and processes much faster, eliminating the trade-off between information completeness and processing efficiency
Data Source
AI summary
The systems and methods described herein can increase the accuracy of translations. Implementations can result in fewer input audio signals being transmitted between devices of a network. Reducing the number of redundant translation requests that are sent between the devices of a network can save bandwidth and other computational resources by processing fewer input audio signals.