Parallel TTS Model Sampling Phoneme Durations for Alignment
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Solution Overview
Problem
Traditional speech synthesis models, particularly autoregressive models, face challenges such as poor scalability, error propagation, limited diversity in synthetic speech, and difficulties with audio-text alignment, especially in parallel architectures and with limited data sets.
Innovation Solution
The development of end-to-end, partially parallel text-to-speech (TTS) models that use unsupervised alignment and generative models to sample phoneme durations and pitch, energy, and other speech properties, incorporating data augmentation and Hidden Markov Methods to improve alignment and diversity, and condition speaker variability dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autoregressive models are used for speech synthesis, then the model can generate output speech based on input text sequence, but the model scales poorly as sequence lengths increase
Solution Approach 1:
The patent segments the speech synthesis process into multiple parallel components: a parallel encoder processes the entire input text sequence simultaneously, a duration predictor breaks down phoneme duration prediction into independent predictions for each text token, and a parallel decoder generates mel-spectrogram frames in parallel. This segmentation eliminates the sequential bottleneck of traditional autoregressive models while maintaining synthesis accuracy.
Solution Approach 2:
The patent introduces a duration dimension as an intermediate variable between text tokens and mel-spectrogram frames. By predicting phoneme durations in this additional dimensional space, the model enables parallel processing of temporal information without requiring sequential autoregressive generation, thus improving scaling efficiency while preserving synthesis reliability.
2Reliability
If autoregressive models are used for speech synthesis, then the model can generate output speech, but a single poorly predicted audio frame may lead to additional errors throughout the entire sequence
Solution Approach 1:
The patent extracts the duration prediction function from the main autoregressive generation process into a separate parallel component. The duration predictor independently predicts phoneme durations for all text tokens simultaneously, decoupling temporal information from the sequential mel-spectrogram generation. This extraction prevents error propagation because duration predictions do not depend on previous frame predictions.
Solution Approach 2:
The model performs preliminary duration prediction for all phonemes before generating the mel-spectrogram sequence. By pre-computing phoneme durations in parallel for the entire input sequence, the model establishes a complete temporal framework upfront, preventing cascading errors that would occur if durations were predicted sequentially during generation.
3Measurement precision
If traditional alignment tools are used, then audio-text alignment can be achieved, but the tools are limited by language or alphabet availability
Solution Approach 1:
The patent implements self-service alignment where the neural network model learns to align audio and text automatically during training without requiring external alignment tools. The attention mechanism and duration predictor jointly optimize alignment between text tokens and mel-spectrogram frames, making the system self-sufficient and language-agnostic. This eliminates dependency on pre-trained aligners for specific languages or alphabets.
Solution Approach 2:
The patent creates a universal alignment mechanism through the attention-based duration predictor that works across any language or alphabet. The model processes text tokens and audio frames in a language-independent manner, using learned representations rather than language-specific rules. This universal approach enables the system to handle any language without requiring separate alignment tools.
4Reliability
If autoregressive models are used for speech synthesis, then the model can generate speech output, but the models lack diversity in synthetic speech outcome
Solution Approach 1:
The patent introduces dynamic sampling from the duration distribution during inference. Instead of using fixed or deterministically predicted durations, the model samples from the predicted duration distribution, introducing variability in phoneme lengths. This dynamic approach maintains consistency through the learned distribution while generating diverse synthetic speech outcomes from the same input text.
Solution Approach 2:
The patent changes the duration parameter from a fixed value to a sampled value from a probability distribution. By modifying how the duration parameter is determined (from deterministic prediction to stochastic sampling), the system achieves diversity in speech output while maintaining the underlying linguistic structure and consistency learned during training.
Data Source
AI summary
Generation of synthetic speech from an input text sequence may be difficult when durations of individual phonemes forming the input text sequence are unknown. A predominantly parallel process may model speech rhythm as a separate generative distribution such that phoneme duration may be sampled at inference. Additional information such as pitch or energy may also be sampled to provide improved diversity for synthetic speech generation.