Synthetic Speech Contrastive Stress Markup
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Solution Overview
Problem
Conventional speech synthesis techniques for speech-enabled applications are labor-intensive and lack naturalness, as they require hardcoding audio files and rely on third-party TTS engines, failing to incorporate contrastive stress, which is crucial for human-like speech understanding.
Innovation Solution
A method and apparatus that analyze text inputs to apply contrastive stress by identifying and emphasizing specific words or syllables through increased pitch, amplitude, or duration, allowing developers to specify text transcriptions with tags or annotations for enhanced speech synthesis, blending CPR and TTS techniques for improved naturalness and user understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speech synthesis techniques (CPR or TTS) are used, then speech output can be generated, but the speech lacks naturalness and contrastive stress
Solution Approach 1:
The speech synthesis process is divided into multiple stages: text analysis stage (identifying contrastive words), audio generation stage (selecting and modifying audio segments), and output stage. This segmentation allows each stage to focus on specific tasks, improving naturalness while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary analysis of the text input to identify words that require contrastive stress before audio generation. This advance identification allows the audio synthesis engine to pre-select appropriate audio segments with contrastive stress, ensuring natural speech output without adding complexity to the real-time generation process.
2Reliability
If text analysis is performed to identify contrastive words, then contrastive stress can be applied, but processing time increases
Solution Approach 1:
The text analysis engine automatically identifies contrastive words and generates markup without requiring manual intervention or complex configuration. The system serves itself by using linguistic rules and context analysis to determine which words need contrastive stress, achieving accurate contrastive stress application while minimizing processing overhead.
Solution Approach 2:
The system changes the parameter of text representation by converting plain text into marked-up text with contrastive stress indicators. This parameter transformation enables the audio synthesis engine to quickly identify and apply contrastive stress to specific words without requiring complex real-time analysis during audio generation.
3Reliability
If markup language is used to specify contrastive stress, then speech naturalness improves, but application development complexity increases
Solution Approach 1:
The markup language serves as an intermediary between the text input and audio synthesis processes. It provides a standardized, simple syntax for specifying contrastive stress that developers can easily use without understanding the complex audio processing underlying it. The markup acts as a bridge that simplifies development while enabling natural speech output.
Solution Approach 2:
The markup language is designed to be universal and compatible with existing speech synthesis systems. It can specify contrastive stress while working with both CPR and TTS approaches, making it a multi-functional tool that improves speech naturalness across different synthesis methods without requiring developers to learn multiple specialized formats.
Data Source
AI summary
Techniques for generating synthetic speech with contrastive stress. In one aspect, a speech-enabled application generates a text input including a text transcription of a desired speech output, and inputs the text input to a speech synthesis system. The synthesis system generates an audio speech output corresponding to at least a portion of the text input, with at least one portion carrying contrastive stress, and provides the audio speech output for the speech-enabled application. In another aspect, a speech-enabled application inputs a plurality of text strings, each corresponding to a portion of a desired speech output, to a software module for rendering contrastive stress. The software module identifies a plurality of audio recordings that render at least one portion of at least one of the text strings as speech carrying contrastive stress. The speech-enabled application generates an audio speech output corresponding to the desired speech output using the audio recordings.