Contextual Text-to-Speech Processing via Segment Feature Vectors
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Solution Overview
Problem
Existing text-to-speech (TTS) systems face challenges in efficiently processing large textual works like eBooks, as they struggle to segment text into contextual portions that provide natural and continuous audio output, often resulting in disjointed user experiences due to lack of contextual information.
Innovation Solution
The system segments text data into portions like chapters, paragraphs, and dialogue sections, and uses feature vectors to consider contextual characteristics of adjacent text portions during TTS processing, ensuring more natural and continuous audio output by incorporating context from surrounding text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text is segmented into smaller portions for processing, then processing efficiency is improved, but audio continuity and naturalness deteriorate due to loss of contextual information
Solution Approach 1:
The text is divided into multiple segments that are processed individually through the TTS system. Each segment is handled separately to improve processing efficiency and reduce memory requirements, while still maintaining the ability to produce continuous audio output through proper segmentation boundaries.
Solution Approach 2:
Context vectors are introduced as intermediary data structures that capture contextual information from surrounding text segments. These context vectors are passed between segment processing operations to preserve contextual relationships without requiring all text to be loaded simultaneously, thus maintaining audio naturalness while enabling efficient segmented processing.
2Reliability
If large amounts of text are processed together to maintain context, then audio naturalness is improved, but memory usage and processing time increase
Solution Approach 1:
Contextual information is extracted from the full text and condensed into compact context vectors. Only the essential contextual features are retained and stored, while the bulk of the original text data can be discarded or processed in smaller chunks, significantly reducing memory requirements while preserving the contextual information needed for natural speech synthesis.
Solution Approach 2:
The system transforms the text representation from raw textual data into parameterized context vectors that capture the essential contextual characteristics. This parameter transformation allows the system to work with compressed representations of context that require far less memory than the original text while still enabling natural-sounding speech synthesis.
3Reliability
If contextual information from surrounding text is incorporated, then speech quality is improved, but processing complexity increases
Solution Approach 1:
Context vectors are pre-computed from the surrounding text before the main TTS processing occurs. This preliminary extraction of contextual information separates the complex contextual analysis from the speech synthesis process, allowing the TTS system to receive pre-prepared context data and reduce the overall processing complexity during the critical speech generation phase.
Data Source
AI summary
A text-to-speech (TTS) system that is capable of considering characteristics of various portions of text data in order to create continuity between segments of synthesized speech. The system can analyze text portions of a work and create feature vectors including data corresponding to characteristics of the individual portions and/or the overall work. A TTS processing component can then consider feature vector(s) from other portions when performing TTS processing on text of a first portion, thus giving the TTS component some intelligence regarding other portions of the work, which can then result in more continuity between synthesized speech segments.