Nearspeech

Nearspeech is a term used in speech technology to describe synthetic or automated speech that approaches natural human speech in quality, realism, and expressiveness. It denotes a level of speech output achieved when synthesis methods produce near-human intelligibility, natural prosody, timing, and articulation, often evaluated with listener-based tests and objective metrics.

In practice, nearspeech is associated with advances in neural text-to-speech, voice conversion, and high-quality vocoders, which leverage large, diverse datasets and sample-efficient training, improved prosody modeling, and speaker adaptive techniques. Systems aim to reproduce nuances such as intonation, rhythm, and emotion while maintaining clear articulation and minimal artifacts.

Applications include virtual assistants, accessibility aids for people who are blind or have reading impairments, voice dubbing and streaming, gaming and virtual reality, and any domain requiring natural-sounding synthesized voice.

Challenges include maintaining consistency across long passages, avoiding unnatural exaggeration of prosody, mitigating artifacts from vocoders, ensuring robustness across languages and voices, and reducing computational cost for real-time use. Evaluation often relies on mean opinion scores and objective measures like Mel-cepstral distortion, but true nearspeech is judged most effectively only through perceptual listening tests.

See also: neural text-to-speech, vocoder, prosody, speech synthesis, voice conversion.