Speech Recognition Data Evaluation via Confidence and Statistical Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In speech recognition systems, determining which tasks to offload between client and server devices is challenging due to the need to balance computing resources, recognition accuracy, and user experience, while also identifying statistically improbable characteristics that may indicate errors in speech recognition data.
Innovation Solution
A method is introduced where a client computing device determines an estimated confidence level for speech recognition data and identifies statistically improbable characteristics, then transfers portions with high confidence levels and unusual characteristics to a server for further evaluation, leveraging the server's more robust capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition data is transferred to a server for evaluation, then recognition accuracy is improved, but network bandwidth usage increases
Solution Approach 1:
The system applies different quality levels of processing to different portions of speech data based on their characteristics. High-confidence portions with normal characteristics are processed locally with minimal server interaction, while only portions with statistically improbable characteristics are transferred to the server for enhanced evaluation, optimizing the balance between accuracy and bandwidth usage
Solution Approach 2:
Instead of evaluating all speech recognition data on the server, the system performs partial evaluation by identifying and transferring only the specific portions that contain statistically improbable characteristics. This selective approach reduces network bandwidth consumption while maintaining recognition accuracy for problematic cases
2Productivity
If speech recognition tasks are offloaded to a server, then processing capability is improved, but latency increases
Solution Approach 1:
The client device performs preliminary filtering and identification of statistically improbable characteristics before transferring data to the server. This preliminary action reduces the volume of data requiring server processing and enables faster, more targeted server evaluation, thereby reducing overall latency
Solution Approach 2:
The speech recognition data is segmented into portions based on confidence levels and statistical characteristics. Only specific segments with improbable characteristics are offloaded to the server, while other segments are processed locally, reducing the total data transfer time and latency
3Measurement precision
If all speech recognition data is evaluated on the server, then recognition accuracy is improved, but computing resource usage increases
Solution Approach 1:
The system implements quality-based processing where the level of server evaluation is matched to the needs of each data portion. Only portions with statistically improbable characteristics receive intensive server-side computing resources, while normal portions are handled efficiently by the client, optimizing computing resource allocation
Solution Approach 2:
The client device performs self-service by independently evaluating high-confidence speech recognition portions and only submitting problematic cases to the server. This reduces the overall computing burden on the server while maintaining accuracy for cases that require enhanced processing
Data Source
AI summary
Computing devices and methods for providing speech recognition data from one computing device to another device are disclosed. In one disclosed embodiment, audio input is received at a client device and processed to generate speech recognition data. An estimated confidence level is determined for a portion of the data, where the confidence level exceeds a predetermined confidence threshold corresponding to a valid result. At least one statistically improbable characteristic associated with the portion of data is identified. Based on identifying the statistically improbable characteristic, the portion of data is provided to a server computing device for evaluation.