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Book Review · 1Class Review · 1Paper Review · 59

Book Review

처음 만나는 금융공학 ↗

Class Review

Unit 1: Introduction to Agents (Part 1) ↗

Paper Review

이번 주 AI/ML 논문 훑어보기 (26.03.16 ~ 03.22) ↗이번 주 AI/ML 논문 훑어보기 (26.03.09 ~ 03.15) ↗이번 주 AI/ML 논문 훑어보기 (26.03.02 ~ 03.08) ↗이번 주 AI/ML 논문 훑어보기 (26.02.23 ~ 03.01) ↗이번 주 AI/ML 논문 훑어보기 (26.02.09 ~ 02.15) ↗이번 주 AI/ML 논문 훑어보기 (26.02.02 ~ 02.08) ↗이번 주 AI/ML 논문 훑어보기 (26.01.26 ~ 02.01) ↗RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing ↗Aasist: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks ↗VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers ↗VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment ↗Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling ↗Natural language guidance of high-fidelity text-to-speech with synthetic annotations ↗Should you use a probabilistic duration model in TTS? Probably! Especially for spontaneous speech ↗Neural Codec Langauge Models are Zero-Shot Text to Speech Synthesizers ↗High Fidelity Neural Audio Compression ↗SoundStream: An End-to-End Neural Audio Codec ↗Imaginary Voice: Face-styled Diffusion Model for Text-to-Speech ↗VoiceMe: Personalized voice generation in TTS ↗NN-KOG2P: A Novel Grapheme-To-Phoneme Model for Korean Language ↗Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech ↗Label Embedding for Chinese Grapheme-to-Phoneme Conversion ↗Almost Unsupervised Text to Speech and Automatic Speech Recognition ↗Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge ↗Multilingual grapheme-to-phoneme conversion with byte representation ↗Unified Mandarin TTS Front-end Based on Distilled BERT Model ↗A bi-directional LSTM approach for polyphone disambiguation in mandarin chinese ↗Sequence-to-sequence neural net models for grapheme-to-phoneme conversion ↗Grapheme-to-phoneme conversion using Long Short-Term Memory recurrent neural networks ↗Multi-Speaker Emotional Text-to-Speech Synthesizer ↗Style Tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis ↗Generalization Ability of MOS Prediction Networks ↗LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech ↗MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network ↗FastPitchFormant: Source-Filter Based Decomposed Modeling for Speech Synthesis ↗FastPitch: Parallel text-to-speech with pitch prediction ↗Meta-StyleSpeech: Multi-Speaker Adaptive Text-to-Speech Generation ↗HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis ↗Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling ↗Parallel Tacotron: Non-autoregressive and controllable TTS ↗AdaSpeech 3: Adaptive text to speech for spontaneous style ↗AdaSpeech 2: Adaptive text to speech with untranscribed data ↗AdaSpeech: Adaptive text to speech for custom voice ↗Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis ↗Glow-TTS: A generative flow for text-to-speech via monotonic alignment search ↗Melgan: Generative adversarial networks for conditional waveform synthesis ↗Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram ↗Adversarial audio synthesis ↗FastSpeech 2: Fast and high-quality end-to-end text to speech ↗FastSpeech: Fast, robust and controllable text to speech ↗All about Tacotron 1,2 ↗Informer: Beyond Efficient Transformer for Long Sequence Time-Series ↗Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models ↗Adversarial sparse transformer for time series forecasting ↗N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting ↗Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting ↗Stock Market Forecasting Using Computational Intelligence: A Survey ↗End-to-end Music-mixed Speech Recognition ↗Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends ↗