Diffusion Spectrogram Voice Synthesis for Accent-Personalized TTS
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Solution Overview
Problem
Current text-to-speech (TTS) and speech recognition (SR) models face limitations due to the scarcity of fine-grained training data for various accents and dialects, leading to biased and less personalized performance, particularly in scenarios where voice data quality and variability are crucial.
Innovation Solution
Utilizing diffusion models to generate synthetic mel-spectrograms that can be used to train TTS and SR models, allowing for the creation of personalized and unbiased voice data through both conditioned and unconditioned generation techniques, enabling control over accent, pitch, and tone characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional TTS and SR models are trained with limited available voice data, then the models can be developed with simpler data collection processes, but the models exhibit biased performance and lack personalization for various accents and dialects
Solution Approach 1:
The patent uses diffusion models to generate synthetic spectrograms that copy and replicate the characteristics of real voice data. These synthetic spectrograms serve as artificial training samples that mimic the statistical properties and acoustic features of actual human speech, enabling model training without requiring additional real voice recordings from diverse speakers.
Solution Approach 2:
The diffusion model allows manipulation of conditioning parameters such as accent type, dialect, pitch, and tone to generate spectrograms with specific characteristics. By changing these parameters during the generation process, the system can produce diverse training data covering various accents and dialects without collecting new real-world recordings for each scenario.
2Adaptability or versatility
If more diverse voice data is collected to improve model personalization and reduce bias, then better TTS and SR performance across accents can be achieved, but the data collection process becomes more complex and resource-intensive
Solution Approach 1:
The system uses the diffusion model to self-generate the diverse training data it needs without external data collection efforts. The model automatically produces synthetic spectrograms with various accent and dialect characteristics by adjusting its conditioning parameters, eliminating the need for manual recruitment, recording, and processing of diverse voice samples.
Solution Approach 2:
The diffusion model is pre-trained on a base dataset to learn the underlying distributions of different accents and dialects. This preliminary training enables the model to subsequently generate diverse spectrograms on demand without requiring actual data collection campaigns for each target accent or dialect scenario.
3Productivity
If synthetic spectrograms are generated using diffusion models, then scalable and customizable training data can be produced without specific voice samples, but the generation process requires advanced machine learning models and computational resources
Solution Approach 1:
The diffusion model serves multiple functions: it generates synthetic spectrograms for training, enables customization of accent and dialect characteristics, and can produce unlimited diverse samples. This single model replaces the need for separate data collection, processing, and annotation pipelines that would traditionally be required to create diverse training datasets.
Data Source
AI summary
Techniques for performing voice synthesis from diffusion generated spectrograms are disclosed. A set of curated audio samples is accessed. A set of spectrograms is also accessed. These spectrograms are based on the set of curated audio samples. A synthetic spectrogram is generated by feeding, as input, the set of spectrograms into a diffusion model, which generates the synthetic spectrogram. An audio file is then generated. The audio file is representative of a synthetic voice. The audio file is generated by feeding, as input, the synthetic spectrogram and input text to a text-to-speech model, which generates the audio file.