25 citations · 42 across the 12 of their papers we have counts for
12 papers
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
Xin Wang, Héctor Delgado, Hemlata Tak +26
ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce…
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…
Towards single integrated spoofing-aware speaker verification embeddings
Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…
SNAC: Speaker-normalized affine coupling layer in flow-based architecture for zero-shot multi-speaker text-to-speech
Byoung Jin Choi, Myeonghun Jeong, Joun Yeop Lee +1
Zero-shot multi-speaker text-to-speech (ZSM-TTS) models aim to generate a speech sample with the voice characteristic of an unseen speaker. The main challenge of ZSM-TTS is to incr…
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…
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…
Diff-TTS: A Denoising Diffusion Model for Text-to-Speech
Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon +2
Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness…