12 papers
DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech
Junwon Moon, Seungbeom Kim, Yejin Lee +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…
Beyond Binary Instrument QA: Probing Instrument Grounding in Music Audio-Language Models
Yujun Lee, Joonhyeok Shin, Hyoeun Kim +1
Recent music audio-language models achieve high accuracy on instrument question-answering benchmarks, but it remains unclear whether this reflects robust audio grounding or benchma…
WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models
Hanna Lee, Tan Dat Nguyen, Jaehoon Kang +1
Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically with sequence length due to f…
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…
Whisper-CD: Accurate Long-Form Speech Recognition using Multi-Negative Contrastive Decoding
Hoseong Ahn, Jeongyun Chae, Yoonji Park +1
Long-form speech recognition with large encoder-decoder models such as Whisper often exhibit hallucinations, repetition loops, and content omissions. These errors can accumulate an…
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…