Text-to-Speech Attention Alignment for Natural Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text-to-speech (TTS) systems struggle to synthesize speech that sounds realistic and natural, particularly when training data for non-speech sounds is limited.
Innovation Solution
The method employs an encoder module, an attention module, and a decoder module, utilizing a conformer with self-attention layers and a recurrent neural network to process text, apply threshold and activation functions to the attention vector, and derive context vectors for improved alignment, thereby enhancing the synthesis of speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional TTS algorithms are used with limited training data, then the system can operate with small datasets, but the speech synthesis quality becomes unrealistic and unnatural
Solution Approach 1:
The patent applies preliminary action by pre-training the model on large corpora of speech data to learn fundamental speech patterns, prosody, and acoustic characteristics before fine-tuning on specific target speech data. This preliminary learning phase enables the model to achieve high-quality synthesis even when the final training dataset is small, as the model has already internalized general speech generation capabilities
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting model parameters such as attention weights, duration predictions, and spectral characteristics during the synthesis process. The system modifies these parameters based on the input text and learned patterns to generate natural-sounding speech, allowing high-quality output without requiring extensive training data for every possible speech scenario
2Measurement precision
If complex attention mechanisms are applied to improve alignment accuracy, then timing precision improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the attention mechanism into distinct functional components: one module dedicated to predicting speech duration based on text input, and another module for generating spectral content. This segmentation allows each component to specialize in specific aspects of speech synthesis, improving timing accuracy through dedicated duration prediction while keeping individual module complexity manageable
Solution Approach 2:
The patent introduces an intermediary duration prediction module that acts as a mediator between the text encoder and the speech decoder. This intermediary component translates text semantic information into temporal duration predictions, which then guide the main synthesis process. By inserting this intermediary layer, the system achieves precise timing control without requiring the entire model to be excessively complex
Data Source
AI summary
A method for synthesising speech from text includes receiving text and encoding, by way of an encoder module, the received text. The method further includes determining, by way of an attention module, a context vector from the encoding of the received text, wherein determining the context vector comprises at least one of: applying a threshold function to an attention vector and accumulating the thresholded attention vector, or applying an activation function to the attention vector and accumulating the activated attention vector. The method further includes determining speech data from the context vector.