@shekofteh.bsky.social🎙️ Can phase space dynamics boost ASR?
Most speech systems ignore signal phase. In our paper in SIVP, we use Recurrence Plots + 2D Adaptive Wavelets to capture non-linear dynamics.
📄 Read more: link.springer.com/article/10.1...
@detoxima2025.bsky.socialSuno AI 음성 생성 기능 3가지 활용법 공개
#SunoAI #AI음성 #인공지능 #SpeechAI #AI기술 #ArtificialIntelligence #VoiceGeneration
@shekofteh.bsky.socialReached ~87% test accuracy and ~93% validation accuracy on spontaneous speech with our proposed CNN framework.
Demonstrates that clinical AI needs both acoustic timbre and phonetic context.
Check out the full research here: link.springer.com/article/10.1...
@shekofteh.bsky.socialLow-level acoustic features (MFCCs) only tell half the story in voice pathology detection.
What happens when we introduce phonetic-based features (PPPs) into deep learning models?
🧵 Our paper in IJST (Springer) explores this:
link.springer.com/article/10.1...
@shekofteh.bsky.socialKey Numbers:
• ~85% accuracy on challenging test sets
• ~92% accuracy on evaluation sets
Proving that spontaneous speech isn’t just “noise”—it’s rich in diagnostic
Check out the paper here: link.springer.com/article/10.1...
@shekofteh.bsky.socialIn our new paper in IEEE Access, we use Log-Area Ratios (LARs) + a novel Conditional Speaker Normalization (CSN) conditioned on speaker proxies (e.g. height) to detect synthetic speech reliably on ASVspoof & FoR.
@shekofteh.bsky.social)
🎙️ Are synthetic speech artifacts evenly distributed across phonemes? Not quite!
In our latest paper in MTAP, we propose a phonetic-driven framework using PPGs & phoneme pruning, boosting anti-spoofing performance on ASVspoof 2019 LA.
@9cv9recruitment.bsky.socialDiscover Meta Muse Voice Transcribe 1.0, how its real-time speech AI works, key features, performance, pricing and use cases.
#Meta, #MuseVoiceTranscribe, #MuseVoiceTranscribe1, #MetaAI, #MetaAIResearch, #VoiceAI, #SpeechAI,