2 citations · 2 across the 5 of their papers we have counts for
5 papers
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.…
KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMs
Haechan Kim, Seungjun Chung, Inkyu Park +2
Speech language models (SpeechLMs) have achieved substantial progress by extending large language models (LLMs) to the speech modality. However, SpeechLM evaluation remains heavily…
Raon-Speech Technical Report
Beomsoo Kim, Changho Choi, Dohyun Kim +23
We present Raon-Speech, a top-performing 9B-parameter speech language model (SpeechLM) for English and Korean speech understanding, answering, and generation, and Raon-SpeechChat,…
DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors
Keon Lee, Dong Won Kim, Jaehyeon Kim +2
Large-scale latent diffusion models (LDMs) excel in content generation across various modalities, but their reliance on phonemes and durations in text-to-speech (TTS) limits scalab…
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech
Jaehyeon Kim, Keon Lee, Seungjun Chung +1
With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach…