Onur Babacan is a Head of Research and seasoned speech and audio ML engineer with 12 years of R&D experience turning advanced speech research into production-grade systems. He has led TTS and generative audio efforts across startups and labs, owning the full pipeline from data acquisition and signal-processing feature design to PyTorch model development and perceptual/crowdsourced evaluation. His background blends deep signal-processing roots (MSc from ETH Zürich) with hands-on neural vocoder and TTS work at Qualcomm Research, LOVO AI, and Sanas. Known for shipping novel neural vocoders and production TTS products, he bridges research rigor with practical deployment in multilingual and commercial settings. Based in Seoul, he combines academic PhD-level speech expertise with startup agility, often contributing cross-disciplinary solutions like mobile audio algorithms and embedded firmware earlier in his career. Colleagues describe him as a research leader who consistently operationalizes complex audio models into reliable, evaluated products.
12 years of coding experience
9 years of employment as a software developer
BSc Telecommunications Electronics, BSc Telecommunications Electronics at Izmir Institute of Technology
Doctor of Philosophy (PhD) Candidate Speech Processing, Doctor of Philosophy (PhD) Candidate Speech Processing at UMONS
MSc Biomedical Engineering Audio Processing, MSc Biomedical Engineering Audio Processing at ETH Zürich
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