Deep Voice Neural Text-to-Speech System Real-Time Inference
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Solution Overview
Problem
Current text-to-speech (TTS) systems are labor-intensive to develop, rely on complex hand-engineered features, and cannot perform real-time conversions, limiting their applications and efficacy.
Innovation Solution
The development of a fully neural text-to-speech system, Deep Voice, which uses phonemes, stress annotations, phoneme durations, and fundamental frequency as features, allowing for real-time inference and adaptation to new datasets without manual annotation, and achieving high-quality speech synthesis using WaveNet architecture with efficient inference kernels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional multi-stage processing pipelines are used for text-to-speech, then speech synthesis quality can be maintained, but system complexity increases and real-time performance cannot be achieved
Solution Approach 1:
The patent merges multiple traditional TTS processing stages (phoneme conversion, prosody prediction, spectral envelope generation, excitation signal generation) into a single unified neural network model. This consolidation eliminates the need for separate hand-engineered feature extraction and multiple processing modules, enabling real-time speech synthesis while maintaining quality.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods (hand-engineered features, spectral analysis, formant synthesis) with a data-driven neural network approach. The neural network directly maps input text to output speech waveforms, substituting complex mechanical processing pipelines with a learned end-to-end transformation.
2Ease of manufacture
If hand-engineered features are used in traditional TTS systems, then speech quality can be controlled, but development becomes labor-intensive and difficult
Solution Approach 1:
The neural network model performs self-feature-engineering by automatically learning relevant acoustic features and representations from training data. Instead of requiring manual design of phoneme features, prosody parameters, and spectral characteristics, the model discovers and utilizes these features autonomously during training, greatly simplifying development.
Solution Approach 2:
The patent transitions from fixed hand-engineered feature parameters to learned adaptive parameters. The neural network learns optimal feature representations and transformation parameters from data, allowing the system to adapt to different speakers, languages, and speech styles without manual re-engineering of feature extraction pipelines.
3Manufacturing precision
If WaveNet architecture is used for high-quality speech synthesis, then speech quality improves, but inference speed decreases significantly
Solution Approach 1:
The patent segments the speech synthesis process into parallel independent computations where possible. The neural network processes different frequency bands and time steps in a structured manner that enables efficient computation, breaking down the complex WaveNet transformation into manageable operations that can be executed faster.
Solution Approach 2:
The patent utilizes the periodic and repetitive structure of speech signals to optimize inference. By leveraging the autocorrelative nature of speech and the periodic pattern of neural network operations, the system can compute speech frames more efficiently, achieving faster inference while maintaining the high-quality characteristics of WaveNet.
Data Source
AI summary
Embodiments of a production-quality text-to-speech (TTS) system constructed from deep neural networks are described. System embodiments comprise five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For embodiments of the segmentation model, phoneme boundary detection was performed with deep neural networks using Connectionist Temporal Classification (CTC) loss. For embodiments of the audio synthesis model, a variant of WaveNet was created that requires fewer parameters and trains faster than the original. By using a neural network for each component, system embodiments are simpler and more flexible than traditional TTS systems, where each component requires laborious feature engineering and extensive domain expertise. Inference with system embodiments may be performed faster than real time.