Monday 03 March 2025
Scientists have made a significant breakthrough in developing a new speech synthesis system that can generate human-like speech with precise control over its prosody, or the rhythm and intonation of speech.
The new system, called DrawSpeech, uses sketches drawn by users to guide the generation process. These sketches provide a rough indication of the desired pitch and energy trends in the synthesized speech, allowing for fine-grained control over the prosody.
Traditionally, text-to-speech systems have relied on complex algorithms and large datasets to generate speech that sounds natural and human-like. However, these systems often struggle to capture the nuances of human speech, such as the subtle variations in pitch and energy that convey emotion and emphasis.
DrawSpeech addresses this limitation by incorporating sketches into the generation process. These sketches are simple drawings that indicate the desired prosody trends in the synthesized speech. For example, a user might draw a sketch with a rising pitch to indicate that the synthesizer should emphasize a particular word or phrase.
The DrawSpeech system uses a combination of machine learning algorithms and diffusion models to generate speech that closely matches the user’s sketches. The system first extracts phoneme-level pitch and energy contours from the input text, which are then used to condition the generation process.
During training, the system is presented with a large dataset of annotated audio recordings, along with corresponding sketches that indicate the desired prosody trends. This allows the system to learn the relationships between the sketches and the prosody patterns in human speech.
When generating speech, DrawSpeech uses these learned relationships to generate speech that matches the user’s sketches. The system can also be fine-tuned during training to prioritize specific aspects of prosody, such as pitch or energy.
The results of this study are impressive, with DrawSpeech able to generate speech that sounds natural and human-like. In addition, the system is highly customizable, allowing users to tailor the prosody of their synthesized speech to suit their needs.
This technology has significant potential applications in fields such as language learning, voice assistants, and multimedia design. For example, language learners could use DrawSpeech to practice pronunciation and intonation with a more realistic and engaging experience. Voice assistants could use the system to generate speech that better matches users’ preferences for prosody and tone. And multimedia designers could use DrawSpeech to create more nuanced and expressive audio experiences.
Overall, DrawSpeech represents an important step forward in the development of natural-sounding text-to-speech systems.
Cite this article: “DrawSpeech: A Breakthrough in Human-Like Speech Synthesis”, The Science Archive, 2025.
Text-To-Speech, Speech Synthesis, Prosody, Rhythm, Intonation, Machine Learning, Diffusion Models, Sketches, Natural Language Processing, Human-Like Speech.
