5 papers
Discriminative-Generative Target Speaker Extraction with Decoder-Only Language Models
Bang Zeng, Beilong Tang, Wang Xiang +1
Target speaker extraction (TSE) aims to recover the speech of a desired speaker from a mixture given a short enrollment utterance, while speech enhancement (SE) focuses on improvin…
SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantization
Beilong Tang, Xiaoxiao Miao, Xin Wang +1
Voice anonymization protects speaker privacy by concealing identity while preserving linguistic and paralinguistic content. Self-supervised learning (SSL) representations encode li…
Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models
Yuke Lin, Ming Cheng, Ze Li +2
Multi-speaker automatic speech recognition (MS-ASR) faces significant challenges in transcribing overlapped speech, a task critical for applications like meeting transcription and…
LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models
Beilong Tang, Bang Zeng, Ming Li
We propose LauraTSE, an Auto-Regressive Decoder-Only Language Model for Target Speaker Extraction built upon the LauraGPT backbone. LauraTSE employs a small-scale auto-regressive d…
TSELM: Target Speaker Extraction using Discrete Tokens and Language Models
Beilong Tang, Bang Zeng, Ming Li
We propose TSELM, a novel target speaker extraction network that leverages discrete tokens and language models. TSELM utilizes multiple discretized layers from WavLM as input token…