Voice Age Conversion Model for Adaptive Signal Processing
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Solution Overview
Problem
Existing speech information technology systems cannot adaptively process voice signals according to the needs of various users, limiting their ability to convert a speaker's voice into a desired age group.
Innovation Solution
An apparatus and method utilizing a trained voice age conversion model, applied through a processor, to receive and transform a user's voice signal into a target voice signal estimated to be of a pre-inputted desired age, incorporating artificial intelligence and machine learning techniques such as supervised and unsupervised learning, deep neural networks, and generative adversarial networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional voice recognition system is used, then voice signal recognition can be achieved, but the system cannot adaptively process voice signals according to different age groups
Solution Approach 1:
The system performs preliminary training of the voice age conversion model using paired voice data from different age groups before actual use. This pre-training prepares the model to adaptively process voices for different age groups, resolving the contradiction between adaptability and reliability by establishing a foundation of age-specific voice characteristics in advance.
Solution Approach 2:
The system changes parameters of the voice signal (such as pitch, formant frequencies, and spectral characteristics) based on the target age group. By adjusting these acoustic parameters through the trained conversion model, the system achieves reliable age-specific voice processing while maintaining adaptability across different age ranges.
2Reliability
If voice information for a desired age group is not secured, then the system cannot generate accurate voice output for that age group
Solution Approach 1:
The system creates synthetic voice copies for underrepresented age groups by training the conversion model on available voice data and transforming it to match target age characteristics. This copying approach enables the system to generate reliable voice output for age groups where direct voice samples may be limited, thereby expanding coverage while maintaining accuracy.
Solution Approach 2:
The voice age conversion model is designed to handle multiple age groups universally through a single trained system. The model learns general voice transformation patterns that can be applied across different age ranges, allowing the system to maintain reliability across diverse age groups without requiring separate specialized models for each age category.
3Adaptability or versatility
If a trained voice age conversion model is applied to transform voice signals, then adaptability to desired age groups is improved, but system complexity increases
Solution Approach 1:
The trained voice age conversion model serves as an intermediary component between the voice input and output stages. This dedicated intermediary module handles the complex transformation task, allowing the rest of the system to remain relatively simple while achieving high adaptability for voice age conversion.
Solution Approach 2:
The complex model training process is performed in advance as a preliminary action, creating a ready-to-use conversion model. This shifts the computational complexity from real-time operation to an offline training phase, reducing the processing burden during actual voice conversion while maintaining high adaptability.
Data Source
AI summary
According to one embodiment, an apparatus for processing a voice signal includes a display configured to display an image of a user or a character corresponding to the user, a microphone, a speaker configured to output a voice signal of the user, a memory configured to store a trained voice age conversion model, and a processor configured to, based on changing an age of the user or the character displayed on the display, control the display such that the display displays the user or the character corresponding to the changed age. The processor is further configured to determine a first age that is a current age of the user or the character based on the voice signal of the user inputted through the microphone. Accordingly, convenience of a user may be enhanced.