The creation of natural and expressive synthetic voices is a fascinating field of research at the intersection of artificial intelligence, linguistics, and acoustics. Recent progress is impressive, but many technical challenges remain. Let's dive behind the scenes of text-to-speech technology to understand how it works and what obstacles researchers are striving to overcome.
From concatenative synthesis to neural synthesis
The first generations of speech synthesis used a so-called "concatenative" approach. It consisted of recording thousands of small speech segments (phonemes) from a voice actor, and then assembling them to form words and sentences. The result was often robotic and lacked fluidity, as it was difficult to smooth the transitions between segments.
Today, the dominant approach is neural speech synthesis. Deep learning models like WaveNet (developed by DeepMind) and Tacotron (developed by Google) learn to directly generate the audio waveform from the text. These models are trained on huge datasets of human speech and are able to produce strikingly natural voices.
The challenge of prosody
Prosody is one of the most difficult aspects to model. It includes intonation, rhythm, stress, and pauses, which are essential for conveying the meaning and emotion of a sentence. Poor prosody can make a sentence ambiguous or give it an unintentionally sarcastic tone. Neural TTS models are becoming increasingly effective at capturing prosody, but there is still work to be done to achieve the complexity and subtlety of human speech.
Controlling expression
Another major challenge is to give users fine control over the style and expression of the synthetic voice. How can you ask a synthetic voice to speak in a happy, sad, or whispered tone? Researchers are exploring different approaches, such as adding "style tags" to the input text, or using conditional models that can be adjusted to produce different speech styles.
Data scarcity
Training neural TTS models requires huge amounts of high-quality audio data, often tens or hundreds of hours of recordings from a single speaker. This makes creating new voices expensive and time-consuming. In addition, for less common languages, it can be difficult to find enough data to train a high-performing model. Research is therefore focusing on few-shot learning techniques to create new voices from just a few minutes of recording.
Conclusion
The creation of natural synthetic voices is a complex problem that pushes the boundaries of artificial intelligence. Despite the challenges, progress is constant and rapid. At Free TTS, we are closely following these advances to bring you the most natural and expressive voices possible.