3 papers
eess.AS2026
Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech
Semin Kim, Seungjun Chung, Taehong Moon +8
Recent advances in text-to-speech (TTS) models show impressive speech naturalness and quality, yet the role of large-scale open data in driving this progress remains underexplored.…
eess.AS2026
SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models
Seonuk Kim, Yonghyeon Jun, Ju Yeon Kang +3
Large audio language models (LALMs) extend large language models with an audio encoder and large-scale audio data. However, the scarcity of high-quality annotated audio data remain…
eess.AS2024
SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech
Minchan Kim, Myeonghun Jeong, Joun Yeop Lee +1
We present SegINR, a novel approach to neural Text-to-Speech (TTS) that addresses sequence alignment without relying on an auxiliary duration predictor and complex autoregressive (…