23 citations · 84 across the 24 of their papers we have counts for
12 papers · 1 filter
Speaking Longer Than Learned: Training-Free Long-Form Generation for Full-Stream Text-to-Speech
Jeseung Lim, Jiseong Woo, Heeseung Kim
Recent full-stream text-to-speech (TTS) models enable low-latency personalized synthesis as input text arrives. However, sustaining generation beyond the lengths seen during traini…
Beyond Fixed Targets: On-Policy One-Shot Speaker Personalization for Autoregressive Text-To-Speech Models
Sangkwon Park, Che Hyun Lee, Heeseung Kim +1
One-shot personalization of autoregressive (AR) text-to-speech (TTS) models remains challenging because supervised fine-tuning (SFT) on a single reference pair rapidly degrades gen…
SSEmb: Training a Generalizable Speech Retrieval Model Using Only Automatic Speech Recognition Data
Sangkwon Park, Dongwook Lee, Youngho Cho +3
End-to-end speech-to-text retrieval is becoming an important capability for voice agents, allowing spoken queries to retrieve relevant documents without intermediate transcription.…
Phrase-Localized Language-Contrastive Guidance: Training-Free Localized Accent Control for Code-Switching Text-to-Speech
Che Hyun Lee, Sangkwon Park, Donghun Kang +4
Current speech synthesis struggles with code-switching, which mixes a foreign language phrase into a primary language utterance, causing the phrase to be spoken with the primary la…
HEAR Who Said What: Unlocking Speaker-Attributed Reasoning via Counterfactual Voice Grounding
Dongwook Lee, Sangkwon Park, Eunwoo Song +6
Speech Language Models (SLMs) are increasingly deployed in multi-speaker environments, yet their ability to attribute speech to the correct speaker and reason over speaker identiti…
DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech
Junwon Moon, Yejin Lee, Seungbeom Kim +4
Autoregressive (AR) text-to-speech (TTS) models generate discrete speech tokens sequentially, which makes inference slow and can degrade robustness, since local errors propagate to…
From Awareness to Adherence: Bridging the Context Gap in Spoken Dialogue Systems via Context-Aware Decoding
Che Hyun Lee, Heeseung Kim, Sungroh Yoon
Despite the success of end-to-end (E2E) spoken dialogue systems, maintaining strict context adherence in multi-round conversations remains a challenge. While prior works attribute…
NaturalFlow: Reducing Disruptive Pauses for Natural Speech Flow in Simultaneous Speech-to-Speech Translation
Dongwook Lee, Youngho Cho, Sangkwon Park +2
Simultaneous speech-to-speech translation aims to enable near-real-time communication by minimizing latency, offering a compelling, real-time alternative to the high latency of con…
TLDR: Compressing Audio Tokens for Efficient Autoregressive Text-to-Speech
Yejin Lee, Junwon Moon, Hyoeun Kim +3
Codec-based autoregressive (AR) speech language models have achieved strong text-to-speech (TTS) quality by modeling speech as sequences of discrete audio tokens with large pretrai…
Omni-Persona: Systematic Benchmarking and Improving Omnimodal Personalization
Yeongtak Oh, Dongwook Lee, Sangkwon Park +2
While multimodal large language models have advanced across text, image, and audio, personalization research has remained primarily vision-language, with unified omnimodal benchmar…
Still Between Us? Evaluating and Improving Voice Assistant Robustness to Third-Party Interruptions
Dongwook Lee, Eunwoo Song, Che Hyun Lee +2
While recent Spoken Language Models (SLMs) have been actively deployed in real-world scenarios, they lack the capability to discern Third-Party Interruptions (TPI) from the primary…
Mask2Flow-TSE: Two-Stage Target Speaker Extraction with Masking and Flow Matching
Junwon Moon, Seungbeom Kim, Hansol Park +4
Target speaker extraction (TSE) extracts the target speaker's voice from overlapping speech given a reference utterance. Existing masking-based approaches are lightweight and effec…