논문 링크: https://arxiv.org/abs/2006.04558 FastSpeech 2: Fast and High-Quality End-to-End Text to SpeechNon-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duratioarxiv.org 1. 서론1.1 논문 선정 이유FastSpeech 2는 ..
논문 링크: https://arxiv.org/abs/1905.09263 FastSpeech: Fast, Robust and Controllable Text to SpeechNeural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from the mel-spectrogram usarxiv.org 1. 서론1.1 논문 선정 이유FastSpeech는 딥러닝 기반 ..
논문 링크: https://arxiv.org/abs/1409.3215 Sequence to Sequence Learning with Neural NetworksDeep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paparxiv.org 1. 서론1.1 논문 선정 이유자연어 처리 분야에서 가장 강력한 모델로 평가받..
논문 링크: https://arxiv.org/abs/1505.04597 U-Net: Convolutional Networks for Biomedical Image SegmentationThere is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotatedarxiv.org 1. 서론1.1 논문 선정 이유효율적인 모델 구조 설계..
