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.…
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…
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…
Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models
Heeseung Kim, Che Hyun Lee, Sangkwon Park +4
Recent advancements in multi-turn voice interaction models have improved user-model communication. However, while closed-source models effectively retain and recall past utterances…
NanoVoice: Efficient Speaker-Adaptive Text-to-Speech for Multiple Speakers
Nohil Park, Heeseung Kim, Che Hyun Lee +3
We present NanoVoice, a personalized text-to-speech model that efficiently constructs voice adapters for multiple speakers simultaneously. NanoVoice introduces a batch-wise speaker…
VoiceGuider: Enhancing Out-of-Domain Performance in Parameter-Efficient Speaker-Adaptive Text-to-Speech via Autoguidance
Jiheum Yeom, Heeseung Kim, Jooyoung Choi +3
When applying parameter-efficient finetuning via LoRA onto speaker adaptive text-to-speech models, adaptation performance may decline compared to full-finetuned counterparts, espec…
VoiceTailor: Lightweight Plug-In Adapter for Diffusion-Based Personalized Text-to-Speech
Heeseung Kim, Sang-gil Lee, Jiheum Yeom +3
We propose VoiceTailor, a parameter-efficient speaker-adaptive text-to-speech (TTS) system, by equipping a pre-trained diffusion-based TTS model with a personalized adapter. VoiceT…
UnitSpeech: Speaker-adaptive Speech Synthesis with Untranscribed Data
Heeseung Kim, Sungwon Kim, Jiheum Yeom +1
We propose UnitSpeech, a speaker-adaptive speech synthesis method that fine-tunes a diffusion-based text-to-speech (TTS) model using minimal untranscribed data. To achieve this, we…
Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Data
Sungwon Kim, Heeseung Kim, Sungroh Yoon
We propose Guided-TTS 2, a diffusion-based generative model for high-quality adaptive TTS using untranscribed data. Guided-TTS 2 combines a speaker-conditional diffusion model with…
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
Heeseung Kim, Sungwon Kim, Sungroh Yoon
We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does not require any transcript of target speaker using classifier guidance. Guided-TTS combines an unconditio…