Neural TTS with Context-Sensitive Text Embeddings
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Solution Overview
Problem
Traditional neural Text-to-Speech (TTS) systems face challenges in generating natural and expressive speech for out-of-domain texts due to limited training data and a one-to-many mapping problem, resulting in issues like strange prosody and incorrect pronunciation.
Innovation Solution
Incorporating context-sensitive text information, such as word level and sentence level text information, into the neural TTS system through pre-trained word embeddings and grammatical parsing, to enhance the model's generalization ability and provide richer text context for speech generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural TTS systems are trained with limited text data, then the model can be trained faster and with less computational resources, but the generalization ability deteriorates leading to strange prosody and incorrect pronunciation for out-of-domain texts
Solution Approach 1:
The patent applies preliminary action by pre-training word embedding models and performing grammatical parsing on large text corpora before the main TTS training. This preprocessing extracts contextual information, word-level features, and sentence-level structures in advance, which are then integrated into the TTS model to improve generalization without requiring extensive domain-specific training data.
Solution Approach 2:
The patent introduces contextual information, word embedding vectors, and grammatical parsing results as intermediary representations between the input text and speech output. These intermediaries capture semantic and syntactic patterns that bridge the gap between limited training data and robust out-of-domain performance, enabling the model to generalize better.
2Manufacturing precision
If context-sensitive text information is incorporated into the neural TTS system, then the quality and naturalness of synthesized speech improves, but the device complexity increases due to additional preprocessing and model components
Solution Approach 1:
The patent segments the text processing into multiple independent modules: word embedding generation, grammatical parsing, contextual information extraction, and speech synthesis. Each module handles a specific aspect of text understanding, allowing the complex task to be divided into manageable components that can be processed separately and integrated systematically.
Solution Approach 2:
The patent adds new dimensions to the text representation by incorporating word-level embeddings and sentence-level grammatical structures alongside the traditional phoneme-level information. This multi-dimensional approach enriches the text feature space, enabling the model to capture prosody and pronunciation patterns from multiple linguistic levels simultaneously.
3Reliability
If multi-level text information is processed through pre-trained word embeddings and grammatical parsing, then the robustness for out-of-domain texts improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs computationally intensive tasks such as word embedding pre-training and grammatical parsing in advance, before the actual speech synthesis request. These preprocessing steps create reusable representations that can be quickly accessed during inference, reducing the real-time processing burden while maintaining robustness for out-of-domain texts.
Data Source
AI summary
A method and apparatus for generating speech through neural text-to-speech (TTS) synthesis. A text input may be obtained (1310). Phoneme or character level text information may be generated based on the text input (1320). Context-sensitive text information may be generated based on the text input (1330). A text feature may be generated based on the phoneme or character level text information and the context-sensitive text information (1340). A speech waveform corresponding to the text input may be generated based at least on the text feature (1350).