Search Results for author: Minchan Kim

Found 11 papers, 0 papers with code

MakeSinger: A Semi-Supervised Training Method for Data-Efficient Singing Voice Synthesis via Classifier-free Diffusion Guidance

no code implementations10 Jun 2024 Semin Kim, Myeonghun Jeong, Hyeonseung Lee, Minchan Kim, Byoung Jin Choi, Nam Soo Kim

In this paper, we propose MakeSinger, a semi-supervised training method for singing voice synthesis (SVS) via classifier-free diffusion guidance.

Singing Voice Synthesis

Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models

no code implementations24 Mar 2024 Minchan Kim, Minyeong Kim, Junik Bae, Suhwan Choi, Sungkyung Kim, Buru Chang

Subsequently, ESREAL computes token-level hallucination scores by assessing the semantic similarity of aligned regions based on the type of hallucination.

Hallucination Semantic Similarity +1

Utilizing Neural Transducers for Two-Stage Text-to-Speech via Semantic Token Prediction

no code implementations3 Jan 2024 Minchan Kim, Myeonghun Jeong, Byoung Jin Choi, Semin Kim, Joun Yeop Lee, Nam Soo Kim

We also delve into the inference speed and prosody control capabilities of our approach, highlighting the potential of neural transducers in TTS frameworks.

EM-Network: Oracle Guided Self-distillation for Sequence Learning

no code implementations14 Jun 2023 Ji Won Yoon, Sunghwan Ahn, Hyeonseung Lee, Minchan Kim, Seok Min Kim, Nam Soo Kim

We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning.

Decoder Machine Translation +2

Adversarial Speaker-Consistency Learning Using Untranscribed Speech Data for Zero-Shot Multi-Speaker Text-to-Speech

no code implementations12 Oct 2022 Byoung Jin Choi, Myeonghun Jeong, Minchan Kim, Sung Hwan Mun, Nam Soo Kim

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 with a set of pre-defined speakers.

Fully Unsupervised Training of Few-shot Keyword Spotting

no code implementations6 Oct 2022 Dongjune Lee, Minchan Kim, Sung Hwan Mun, Min Hyun Han, Nam Soo Kim

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 keywords with only a few enrollment samples.

Keyword Spotting Metric Learning +1

Disentangled Speaker Representation Learning via Mutual Information Minimization

no code implementations17 Aug 2022 Sung Hwan Mun, Min Hyun Han, Minchan Kim, Dongjune Lee, Nam Soo Kim

The experimental results show that fine-tuning with a disentanglement framework on a existing pre-trained model is valid and can further improve performance.

Disentanglement Speaker Recognition +2

Expressive Text-to-Speech using Style Tag

no code implementations1 Apr 2021 Minchan Kim, Sung Jun Cheon, Byoung Jin Choi, Jong Jin Kim, Nam Soo Kim

In this work, we propose StyleTagging-TTS (ST-TTS), a novel expressive TTS model that utilizes a style tag written in natural language.

Language Modelling TAG

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