3 citations · 7 across the 12 of their papers we have counts for
12 papers
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.…
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…
Avocodo: Generative Adversarial Network for Artifact-free Vocoder
Taejun Bak, Junmo Lee, Hanbin Bae +3
Neural vocoders based on the generative adversarial neural network (GAN) have been widely used due to their fast inference speed and lightweight networks while generating high-qual…
Hierarchical and Multi-Scale Variational Autoencoder for Diverse and Natural Non-Autoregressive Text-to-Speech
Jae-Sung Bae, Jinhyeok Yang, Tae-Jun Bak +1
This paper proposes a hierarchical and multi-scale variational autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to generate natural speech with diverse speakin…
Into-TTS : Intonation Template Based Prosody Control System
Jihwan Lee, Joun Yeop Lee, Heejin Choi +4
Intonations play an important role in delivering the intention of a speaker. However, current end-to-end TTS systems often fail to model proper intonations. To alleviate this probl…
GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis
Jinhyeok Yang, Jae-Sung Bae, Taejun Bak +2
Recent advances in neural multi-speaker text-to-speech (TTS) models have enabled the generation of reasonably good speech quality with a single model and made it possible to synthe…
Hierarchical Context-Aware Transformers for Non-Autoregressive Text to Speech
Jae-Sung Bae, Tae-Jun Bak, Young-Sun Joo +1
In this paper, we propose methods for improving the modeling performance of a Transformer-based non-autoregressive text-to-speech (TNA-TTS) model. Although the text encoder and aud…
FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis
Taejun Bak, Jae-Sung Bae, Hanbin Bae +2
Methods for modeling and controlling prosody with acoustic features have been proposed for neural text-to-speech (TTS) models. Prosodic speech can be generated by conditioning acou…
A Neural Text-to-Speech Model Utilizing Broadcast Data Mixed with Background Music
Hanbin Bae, Jae-Sung Bae, Young-Sun Joo +2
Recently, it has become easier to obtain speech data from various media such as the internet or YouTube, but directly utilizing them to train a neural text-to-speech (TTS) model is…
Speaking Speed Control of End-to-End Speech Synthesis using Sentence-Level Conditioning
Jae-Sung Bae, Hanbin Bae, Young-Sun Joo +3
This paper proposes a controllable end-to-end text-to-speech (TTS) system to control the speaking speed (speed-controllable TTS; SCTTS) of synthesized speech with sentence-level sp…
Phase-Aware Speech Enhancement with a Recurrent Two Stage Net work
Juntae Kim, Jaesung Bae
We propose a neural network-based speech enhancement (SE) method called the phase-aware recurrent two stage network (rTSN). The rTSN is an extension of our previously proposed two…
End-Point Detection with State Transition Model based on Chunk-Wise Classification
Juntae Kim, Jaesung Bae, Minsoo Hahn
A state transition model (STM) based on chunk-wise classification was proposed for end-point detection (EPD). In general, EPD is developed using frame-wise voice activity detection…