51 citations · 85 across the 9 of their papers we have counts for
9 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…
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…
Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis
Jae-Sung Bae, Joun Yeop Lee, Ji-Hyun Lee +4
Previous works in zero-shot text-to-speech (ZS-TTS) have attempted to enhance its systems by enlarging the training data through crowd-sourcing or augmenting existing speech data.…
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…
An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space
Jihwan Lee, Jae-Sung Bae, Seongkyu Mun +4
With the recent developments in cross-lingual Text-to-Speech (TTS) systems, L2 (second-language, or foreign) accent problems arise. Moreover, running a subjective evaluation for su…
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…
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang +2
Flow-based generative models are composed of invertible transformations between two random variables of the same dimension. Therefore, flow-based models cannot be adequately traine…