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#speech-synthesis

4 curated events
papersTODAY 04:00 UTC

Controllable Dysarthric Speech Synthesis for Speaker-Diverse ASR Training

Researchers propose a speech synthesis method that separates speaker identity from dysarthric articulation patterns, allowing finer control over generated dysarthric speech. The approach conditions synthesis on individual patients, producing varied synthetic speakers to supplement scarce training data for dysarthric speech recognition. This addresses a field bottlenecked by high speaker variability and limited labeled recordings.

speech-synthesis/asr/accessibility/research
papersTODAY 04:00 UTC

DiTAR+ Improves Decoding Stability in Autoregressive Diffusion Speech Synthesis

A new arXiv preprint introduces DiTAR+, a dual-optimization approach for continuous-latent autoregressive diffusion transformer models used in zero-shot speech generation. The method targets the limited decoding stability these models show when producing long utterances or handling complex linguistic input. No results beyond the abstract are described in the report.

speech-synthesis/diffusion-models/autoregressive/zero-shot
papersSEP 10 04:00 UTC

Deterministic prompting for speaker-stable low-resource Greek TTS

Researchers present a data curation recipe combined with deterministic prompting to keep speaker identity stable in text-to-speech systems trained on limited speech data. Modern Greek serves as the test case, since it lacks the curated corpora that underpin state-of-the-art synthesis for high-resource languages. The work addresses quality degradation that TTS models typically show when clean training speech is scarce.

tts/low-resource/speech-synthesis/greek
papersSEP 12 04:00 UTC

Continuous-Time TTS Acoustic Modelling with Neural Controlled Differential Equations

This preprint proposes modelling text-to-speech acoustics in continuous time using neural controlled differential equations, rather than the usual approach of stretching phone-level encoder states to frame-level decoder inputs via predicted durations. The authors argue that length regulation fixes alignment structurally but leaves duration handling as a separate, discrete step. The work is a cross-listed arXiv submission in the cs.AI category.

tts/speech-synthesis/research/neural-odes