Configurable Neural Speech Synthesis via Voice Property Parameters
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Solution Overview
Problem
Conventional text-to-speech (TTS) voices are trained on a single speaker and lack configurability, making it impractical to create diverse voices for multiple devices, especially for small companies or developers with financial or time constraints, leading to similar sounding voices that can cause confusion among users.
Innovation Solution
A configurable neural speech synthesis system that trains a model to generate speech audio conditioned on voice properties such as gender, age, accent, and attitude, using a discriminator to adjust weights and minimize loss functions, allowing for the creation of diverse and distinct voices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional TTS voices are trained on a single speaker, then the training process is simple and fast, but the voice diversity and configurability are limited
Solution Approach 1:
The neural network model is designed to serve multiple functions: it can generate speech for any speaker identity and adapt to various voice properties (gender, age, accent, emotion) through a single unified architecture. The model takes as input both the text to be synthesized and voice property parameters, making it universally applicable across different voice requirements without needing separate models for each speaker
Solution Approach 2:
The system changes the approach from collecting diverse training data for each speaker to instead using parameter control. By introducing voice property parameters (gender, age, accent, emotion) as inputs to the neural network, the system can dynamically adjust the synthesized voice characteristics without requiring separate training datasets for each configuration. This parameter-based control enables versatile voice generation while keeping the training process relatively simple
2Adaptability or versatility
If conventional TTS voices are trained on a single speaker, then the development time and cost are low, but the ability to create unique voices for multiple devices is limited
Solution Approach 1:
The neural network model is pre-trained on a diverse corpus of speech data from multiple speakers beforehand. This preliminary training equips the model with knowledge of various voice characteristics and speech patterns. Once trained, the model can rapidly generate customized voices for different devices by simply adjusting the voice property parameters, eliminating the need for time-consuming retraining for each new voice configuration
Solution Approach 2:
The system uses parameter-based control to enable rapid voice customization. By representing voice characteristics as adjustable parameters (gender, age, accent, emotion), the system can instantly generate unique voices for multiple devices without initiating lengthy training processes. Each device can have its own voice configuration by setting appropriate parameter values, achieving high adaptability while minimizing development time
3Reliability
If conventional TTS voices are trained on a single speaker, then the model is simple to train, but the voices sound similar and cause user confusion
Solution Approach 1:
The neural network is designed as a universal speech synthesis model that can generate diverse voices through parameter control. The model architecture includes components that process both text input and voice property parameters, enabling it to produce acoustically distinct speech for different speakers and voice configurations within a single unified framework
Solution Approach 2:
The system introduces voice property parameters (gender, age, accent, emotion) that directly influence the synthesized speech output. By adjusting these parameters, the model generates acoustically distinct voices with different timbre, pitch, and spectral characteristics. This parameter-based approach ensures voice distinctiveness across multiple devices while maintaining a relatively manageable model structure through efficient use of attention mechanisms and speaker encoding
Data Source
AI summary
A discriminator trained on labeled samples of speech can compute probabilities of voice properties. A speech synthesis generative neural network that takes in text and continuous scale values of voice properties is trained to synthesize speech audio that the discriminator will infer as matching the values of the input voice properties. Voice parameters can include speaker voice parameters, accents, and attitudes, among others. Training can be done by transfer learning from an existing neural speech synthesis model or such a model can be trained with a loss function that considers speech and parameter values. A graphical user interface can allow voice designers for products to synthesize speech with a desired voice or generate a speech synthesis engine with frozen voice parameters. A vector of parameters can be used for comparison to previously registered voices in databases such as ones for trademark registration.