7 citations · 13 across the 8 of their papers we have counts for
8 papers
SPADE: Structured Pruning and Adaptive Distillation for Efficient LLM-TTS
Tan Dat Nguyen, Jaehun Kim, Ji-Hoon Kim +3
The goal of this paper is to introduce SPADE, a framework for Structured Pruning and Adaptive Distillation for Efficient Large Language Model-based text-to-speech (LLM-TTS). Recent…
Accelerating Codec-based Speech Synthesis with Multi-Token Prediction and Speculative Decoding
Tan Dat Nguyen, Ji-Hoon Kim, Jeongsoo Choi +4
The goal of this paper is to accelerate codec-based speech synthesis systems with minimum sacrifice to speech quality. We propose an enhanced inference method that allows for flexi…
Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor
Younglo Lee, Shukjae Choi, Byeong-Yeol Kim +2
We propose a novel speech separation model designed to separate mixtures with an unknown number of speakers. The proposed model stacks 1) a dual-path processing block that can mode…
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks
Soumi Maiti, Yifan Peng, Shukjae Choi +3
We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates tex…
Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in…
Joint unsupervised and supervised learning for context-aware language identification
Jinseok Park, Hyung Yong Kim, Jihwan Park +3
Language identification (LID) recognizes the language of a spoken utterance automatically. According to recent studies, LID models trained with an automatic speech recognition (ASR…
TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose TF-GridNet for speech separation. The model is a novel deep neural network (DNN) integrating full- and sub-band modeling in the time-frequency (T-F) domain. It stacks se…
TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating in the time-frequency (T-F) domain, for monaural talker-independent speaker separation in anechoic con…