Machine-Learning Speech Conversion for Offshore Call Clarity
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Solution Overview
Problem
Offshoring of business services often results in language barriers and miscommunication due to accent, grammar, cultural differences, and electronic connection quality issues between call center agents and customers, leading to inefficiencies and customer dissatisfaction.
Innovation Solution
A machine learning-based system that translates and modifies speech and text in real-time, correcting language errors, adjusting accents, and inserting filler messages to bridge communication gaps, ensuring clear and efficient interactions across different languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If call center services are offshored to reduce costs, then operational costs decrease, but language barriers and miscommunication increase
Solution Approach 1:
The patent introduces machine learning-based translation and speech modification systems as intermediaries between offshore call center agents and customers. These systems real-time translate speech between languages, correct accents, and adjust speech patterns to bridge communication gaps while maintaining cost-effective offshore operations.
Solution Approach 2:
The system dynamically changes speech parameters including language, accent, grammar, and speech patterns through machine learning models. This allows offshore agents to automatically adapt their communication style to match customer preferences and reduce miscommunication without requiring physical relocation of staff.
2Loss of information
If speech modification services are used to correct language errors and accents, then communication clarity improves, but system complexity increases
Solution Approach 1:
The patent extracts the complex speech modification and translation functions into a separate, dedicated service layer. This allows the core call center operations to remain simple while the speech modification capabilities are handled by specialized machine learning services that can be independently managed and improved.
Solution Approach 2:
The speech modification system operates autonomously using machine learning models that automatically detect language errors, correct accents, and adjust speech patterns without requiring manual intervention. The system self-adapts to different languages and speech patterns, reducing the need for complex configuration and management.
3Loss of information
If real-time speech translation and modification is implemented, then language barriers are reduced, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing speech signals for noise reduction, feature extraction, and language identification before translation and modification. This preparation work is done in advance to streamline the subsequent translation process and reduce overall processing time.
Solution Approach 2:
The speech modification and translation process operates continuously in real-time without interrupting the natural flow of conversation. The machine learning models process speech streams continuously, providing near-instantaneous translation and modification to maintain conversational rhythm and minimize delays.
Data Source
AI summary
Technology is described for modifying the speed of an output of a modified message that uses machine learning, comprising receiving message data from a sender to be sent to a recipient. Errors in the message data can be corrected using a normalization service to provide a corrected message. The message data can be converted to and output format, such as a second language using a machine learning translation service. Another operation can comprise setting a speed factor for the message data to be output at a defined rate. The message data can then be sent to the recipient to be reproduced for the recipient at the defined rate.