1 citations · 2 across the 6 of their papers we have counts for
6 papers
SpeechMLC: Speech Multi-label Classification
Miseul Kim, Seyun Um, Hyeonjin Cha +1
In this paper, we propose a multi-label classification framework to detect multiple speaking styles in a speech sample. Unlike previous studies that have primarily focused on ident…
UniCoM: A Universal Code-Switching Speech Generator
Sangmin Lee, Woojin Chung, Seyun Um +1
Code-switching (CS), the alternation between two or more languages within a single speaker's utterances, is common in real-world conversations and poses significant challenges for…
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence
Sangshin Oh, Seyun Um, Hong-Goo Kang
The Gumbel-softmax distribution, or Concrete distribution, is often used to relax the discrete characteristics of a categorical distribution and enable back-propagation through dif…
Adversarial Learning of Intermediate Acoustic Feature for End-to-End Lightweight Text-to-Speech
Hyungchan Yoon, Seyun Um, Changwhan Kim +1
To simplify the generation process, several text-to-speech (TTS) systems implicitly learn intermediate latent representations instead of relying on predefined features (e.g., mel-s…
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations
Se-Yun Um, Jihyun Kim, Jihyun Lee +1
In this paper, we propose a multi-speaker face-to-speech waveform generation model that also works for unseen speaker conditions. Using a generative adversarial network (GAN) with…
Emotional speech synthesis with rich and granularized control
Se-Yun Um, Sangshin Oh, Kyungguen Byun +3
This paper proposes an effective emotion control method for an end-to-end text-to-speech (TTS) system. To flexibly control the distinct characteristic of a target emotion category,…