26 citations · 52 across the 12 of their papers we have counts for
12 papers
SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech
Minchan Kim, Myeonghun Jeong, Joun Yeop Lee +1
We present SegINR, a novel approach to neural Text-to-Speech (TTS) that addresses sequence alignment without relying on an auxiliary duration predictor and complex autoregressive (…
High Fidelity Text-to-Speech Via Discrete Tokens Using Token Transducer and Group Masked Language Model
Joun Yeop Lee, Myeonghun Jeong, Minchan Kim +3
We propose a novel two-stage text-to-speech (TTS) framework with two types of discrete tokens, i.e., semantic and acoustic tokens, for high-fidelity speech synthesis. It features t…
MakeSinger: A Semi-Supervised Training Method for Data-Efficient Singing Voice Synthesis via Classifier-free Diffusion Guidance
Semin Kim, Myeonghun Jeong, Hyeonseung Lee +3
In this paper, we propose MakeSinger, a semi-supervised training method for singing voice synthesis (SVS) via classifier-free diffusion guidance. The challenge in SVS lies in the c…
Utilizing Neural Transducers for Two-Stage Text-to-Speech via Semantic Token Prediction
Minchan Kim, Myeonghun Jeong, Byoung Jin Choi +3
We propose a novel text-to-speech (TTS) framework centered around a neural transducer. Our approach divides the whole TTS pipeline into semantic-level sequence-to-sequence (seq2seq…
Efficient Parallel Audio Generation using Group Masked Language Modeling
Myeonghun Jeong, Minchan Kim, Joun Yeop Lee +1
We present a fast and high-quality codec language model for parallel audio generation. While SoundStorm, a state-of-the-art parallel audio generation model, accelerates inference s…
Transduce and Speak: Neural Transducer for Text-to-Speech with Semantic Token Prediction
Minchan Kim, Myeonghun Jeong, Byoung Jin Choi +2
We introduce a text-to-speech(TTS) framework based on a neural transducer. We use discretized semantic tokens acquired from wav2vec2.0 embeddings, which makes it easy to adopt a ne…
EM-Network: Oracle Guided Self-distillation for Sequence Learning
Ji Won Yoon, Sunghwan Ahn, Hyeonseung Lee +3
We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning. In contrast to con…
Adversarial Speaker-Consistency Learning Using Untranscribed Speech Data for Zero-Shot Multi-Speaker Text-to-Speech
Byoung Jin Choi, Myeonghun Jeong, Minchan Kim +2
Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios wit…
Fully Unsupervised Training of Few-shot Keyword Spotting
Dongjune Lee, Minchan Kim, Sung Hwan Mun +2
For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keyw…
Disentangled Speaker Representation Learning via Mutual Information Minimization
Sung Hwan Mun, Min Hyun Han, Minchan Kim +2
Domain mismatch problem caused by speaker-unrelated feature has been a major topic in speaker recognition. In this paper, we propose an explicit disentanglement framework to unrave…
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
Minchan Kim, Myeonghun Jeong, Byoung Jin Choi +3
Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect. In this paper, we propose a transfer learning framework fo…
Expressive Text-to-Speech using Style Tag
Minchan Kim, Sung Jun Cheon, Byoung Jin Choi +2
As recent text-to-speech (TTS) systems have been rapidly improved in speech quality and generation speed, many researchers now focus on a more challenging issue: expressive TTS. To…