Hyper-structure RNN for Text-to-Speech Pronunciation Accuracy
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Solution Overview
Problem
Conventional text-to-speech applications face challenges in determining pronunciations and lack context awareness, leading to errors and increased computing costs when incorporating additional context or feature information.
Innovation Solution
The use of multiple recurrent neural network (RNN) modules to model and predict phonetic properties from various text inputs, including part-of-speech, phonemes, linguistic prosody, and contextual information, to generate a natural-sounding speech sequence, with a hyper-structure module combining outputs and a global optimization module refining the sequence for improved accuracy and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text-to-speech methods are used, then the system is simple to implement, but the accuracy of pronunciation and context awareness is poor
Solution Approach 1:
The system segments the text-to-speech process into multiple specialized RNN modules, each responsible for specific phonetic properties (phonemes, part-of-speech, linguistic prosody, contextual properties, semantic properties). This segmentation allows each module to focus on a specific aspect, improving overall pronunciation accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The hyper-structure RNN decoder serves as a universal component that integrates outputs from multiple specialized RNN modules. This multi-functional decoder combines various phonetic properties (phonemes, prosody, context, semantics) into a unified speech generation process, improving context awareness and pronunciation accuracy without requiring separate systems for each function.
2Reliability
If additional context or feature information is incorporated, then the naturalness of speech output is improved, but the computing costs increase
Solution Approach 1:
The system divides the processing of additional context and feature information across multiple specialized RNN modules, each handling specific types of data (linguistic prosody, contextual properties, semantic properties). This segmentation distributes the computational load, allowing the system to process rich contextual information for natural speech output while managing computing costs through efficient modular processing.
Solution Approach 2:
The system selectively processes only the necessary phonetic properties and contextual information required for natural speech synthesis, rather than processing all possible data. The hyper-structure RNN decoder integrates only the relevant outputs from each module, avoiding unnecessary computational overhead while maintaining speech naturalness.
3Adaptability or versatility
If multiple RNN modules are used to model phonetic properties, then the context awareness is improved, but the device complexity increases
Solution Approach 1:
The system segments context awareness into multiple specialized RNN modules, each responsible for specific aspects (linguistic prosody, contextual properties, semantic properties). This segmentation enables the system to achieve comprehensive context awareness by combining specialized modules, while the modular architecture keeps individual module complexity manageable and facilitates independent optimization of each component.
Data Source
AI summary
The technology relates to converting text to speech utilizing recurrent neural networks (RNNs). The recurrent neural networks may be implemented as multiple modules for determining properties of the text. In embodiments, a part-of-speech RNN module, letter-to-sound RNN module, a linguistic prosody tagger RNN module, and a context awareness and semantic mining RNN module may all be utilized. The properties from the RNN modules are processed by a hyper-structure RNN module that determine the phonetic properties of the input text based on the outputs of the other RNN modules. The hyper-structure RNN module may generate a generation sequence that is capable of being converting to audible speech by a speech synthesizer. The generation sequence may also be optimized by a global optimization module prior to being synthesized into audible speech.