Recipient-Specific Voice Tone Generation for Telephony Calls
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Solution Overview
Problem
Selecting the correct voice tone during voice communication over devices is difficult due to limited caller information and inconsistent training methods, leading to unmet needs for recipient-specific tone adjustment.
Innovation Solution
A system that extracts voice tone data from user samples, converts speech to text, and generates speech output using a text-to-speech model with adjusted tone, allowing users to save and apply specific tones for different recipients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional caller identification is used, then basic caller information is provided, but voice tone selection remains difficult and ineffective
Solution Approach 1:
The system performs preliminary voice tone training during off-call periods, extracting voice tone characteristics from recorded samples and storing them for later use. This preliminary action prepares the tone data before actual communication needs arise, eliminating the need for difficult real-time tone selection.
Solution Approach 2:
The system automatically extracts voice tone characteristics from recorded voice samples without requiring manual intervention. The tone extraction and selection process is self-service, automatically matching appropriate tones to recipients based on stored training data, freeing users from manual tone selection efforts.
2Adaptability or versatility
If voice tone training is provided, then tone selection capability is improved, but training consistency and effectiveness are insufficient
Solution Approach 1:
The system replaces manual voice tone training with automated machine learning algorithms. Instead of relying on inconsistent human training methods, the system uses computational algorithms to extract voice tone characteristics from recorded samples, ensuring consistent and reliable tone generation across different recipients.
Solution Approach 2:
The system creates copies of voice tone characteristics from recorded samples by extracting and storing tone parameters. These copied tone profiles are then applied to different recipients, ensuring consistent tone reproduction without requiring repeated manual training sessions.
3Ease of operation
If manual tone selection is required, then some tone control is achieved, but communication effectiveness remains unmet
Solution Approach 1:
The system incorporates feedback mechanisms where voice communication outcomes are analyzed to refine tone selection. By monitoring which tones produce better communication results, the system continuously improves its tone matching accuracy, enhancing overall communication effectiveness while maintaining ease of operation.
Solution Approach 2:
The system dynamically adjusts voice tones based on recipient-specific characteristics and communication context. Rather than using static, manually selected tones, the system adapts tones in real-time based on stored training data and communication patterns, significantly improving communication effectiveness.
Data Source
AI summary
An embodiment extracts, from a plurality of voice samples, voice tone data. The embodiment converts, using a speech to text model, a speech input to corresponding text. The embodiment generates a speech output corresponding to the text, the speech output comprising audio generated from the text using a text to speech model and a voice tone generated using the voice tone data.