Adaptive Audio-Generation Model for Low-Data Speaker Emulation
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Solution Overview
Problem
Realistic audio generation neural networks require large training datasets, making it impractical for applications where hours of audio recordings are not available, such as for medical patients with voice-impairing conditions.
Innovation Solution
A sample-efficient, adaptive audio-generation model that can be customized to emulate a new speaker's voice using significantly less training data, allowing adaptation on less powerful hardware like mobile devices, by learning new embedding vectors and fine-tuning neural network parameters with minimal audio recordings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional audio generation neural networks are used, then realistic audio generation is achieved, but large training datasets (hours of audio recordings) are required
Solution Approach 1:
The system performs preliminary training on a large dataset to create a pre-trained audio generation model that learns general speech patterns and characteristics. This pre-trained model serves as a foundation that can be rapidly adapted to new speakers with minimal data, eliminating the need to train from scratch for each speaker while maintaining realistic audio quality
Solution Approach 2:
The system changes the approach from training entire neural network architectures to only updating specific parameters (embedding vectors and selected layers) when adapting to new speakers. This parameter-level adaptation allows the model to capture new speaker characteristics with minimal training data while preserving the general speech generation capabilities learned during pre-training
2Measurement precision
If hours of audio recordings are collected for each speaker, then accurate speaker emulation is achieved, but the process becomes expensive and cumbersome
Solution Approach 1:
The system applies partial action by selectively updating only the most critical components of the model (embedding vectors and selected neural network layers) when adapting to new speakers, rather than retraining the entire model. This partial adaptation achieves accurate speaker emulation with minimal training data, making the process practical for medical patients and other applications where data collection is difficult
3Measurement precision
If large training datasets are used, then model accuracy is improved, but the training process becomes computationally intensive requiring datacenter hardware
Solution Approach 1:
The system segments the training process into two distinct phases: (1) a one-time pre-training phase on large datasets using powerful datacenter hardware to learn general speech patterns, and (2) a rapid adaptation phase for new speakers using minimal data and computational resources. This segmentation allows accurate speaker emulation while reducing the computational power needed for each new speaker adaptation
4Adaptability or versatility
If traditional models are adapted to new speakers, then speaker customization is achieved, but extensive audio recordings are still required
Solution Approach 1:
The system changes the adaptation mechanism from requiring extensive retraining to updating specific parameters (embedding vectors and selected model layers) with minimal data. This parameter-level customization enables the model to adapt to new speakers with only a few seconds or minutes of audio recordings while maintaining full speaker customization capability
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an adaptive audio-generation model. One of the methods includes generating an adaptive audio-generation model including learning a plurality of embedding vectors and parameter values of a neural network using training data comprising first text and audio data representing a plurality of different individual speakers speaking portions of the first text, wherein the plurality of embedding vectors represent respective voice characteristics of the plurality of different individual speakers. The adaptive audio-generation model is adapted for a new individual speaker using adaptation data comprising second text and audio data representing the new individual speaker speaking portions of the second text, the new individual speaker being different from each of the plurality of individual speakers, wherein adapting the audio-generation model includes learning a new embedding vector for the new individual speaker.