Text-to-Speech (TTS): System, Apparatus, Means & Method

OVERVIEW

Researchers at Georgetown University have developed an innovative system to synthesize speech in any voice, in any language, and in any accent. The Text-to-Speech(TTS) system trained on numerous speakers and various languages using neural networks could use any voice to generate speech in any of the languages using any accent. Developed to address the growing demand for natural and versatile speech synthesis, this system is a significant advancement in the field of both artificial intelligence and linguistic processing.

The technical process involves three distinct steps: training Cross-Language TTS, enrolling a new speaker, and synthesizing speech. Step (1) is directed by system builders such as language engineers, while steps (2) and (3) are directed by end-users. Users have the freedom to utilize any of the built-in voices trained and skip step (2).

The application of high quality speech synthesis by the TTS is wide and associated with the proliferation of speech output in computer applications. A practical example of applying the TTS is in language learning applications. By integrating this technology, the platform can dynamically generate speech in various languages and accents, allowing learners to engage with conversations equivalent to those with native speakers. Whether practicing French with a Parisian accent or conversing in Spanish with a Latin American dialect, users can benefit from immersive conversational experiences and realistic interactions that enhance language acquisition and cultural understanding.

BACKGROUND

TTS is in the field of speech output, an unlimited market with high business value. TTS would give any technology giants a competitive edge in the competition for every market in every language.

Traditionally, speech synthesis has relied on parametric synthesis, which analyzes speech parameters dynamically or concatenative synthesis, which involves piecing together pre-recorded segments of speech. While concatenative synthesis has been favored for its higher quality to produce speech indistinguishable from human speech, it necessitates a significant investment in recording speech from a single speaker and restricts the synthesized voice to the domain of the recorded speech, limiting its versatility, especially across languages. In contrast, parametric synthesis offers advantages in terms of requiring less speech for training, and enabling transformation for speech in languages other than the recorded one.

While existing systems excel in single speech synthesis, the challenge lies in extending this capability to multiple languages with consistent voice quality. Finding multilingual voice talent is impractical. TTS used for personalized speech synthesis, allows users to utilize their own voices across various applications.

Benefit

• Widens market reach for speech output business across various applications and enhance user experience
• Enhancing communication versatility and scalability by synthesizing speech across diverse linguistic contexts in various industries
• Increased cost-effectiveness by requiring less training data and resources, also enables lower implementation costs.

Market Application

• Virtual Assistants for companies to enhance the adaptability of synthesized speech, improving user satisfaction.
• Accessibility Tools and assistive technologies to provide customizable speech synthesis solutions tailored to individual users' needs and preferences.
• Multinational business corporations to streamline communication and enhance productivity and collaboration.
• Language Learning Platforms to provide immersive and personalized speech experiences for learners.

Publications

US Patent n.11,605,371