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[논문] Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recognition in Home Environments: Model Development and Validation https://www.jmir.org/2023/1/e46216 Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and ValidIntroduction Growing knowledge that sleep plays a vital role in maintaining well-being and good health, both physical and mental, increases public interest and awareness regarding the importance of sleep to health. Therefore, the deman.. 연구실 공부 2024. 11. 5.
[논문] Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias https://arxiv.org/abs/2306.03509 Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive BiasScaling text-to-speech to a large and wild dataset has been proven to be highly effective in achieving timbre and speech style generalization, particularly in zero-shot TTS. However, previous works usually encode speech into latent using audio codec and usarxiv.org해당 논문을 보고 작성했습니다. Abstract대.. 연구실 공부 2024. 10. 30.
[논문] FVTTS: Face Based Voice Synthesis for Text-to-Speech https://www.isca-archive.org/interspeech_2024/lee24_interspeech.html ISCA Archive - FVTTS : Face Based Voice Synthesis for Text-to-SpeechFVTTS : Face Based Voice Synthesis for Text-to-Speech Minyoung Lee, Eunil Park, Sungeun Hong A face is expressive of individual identity and used in various studies such as identification, authentication, and personalization. Similarly, a voice is a meanswww.is.. 연구실 공부 2024. 10. 28.
[논문] YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone https://arxiv.org/abs/2112.02418 YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyoneYourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (arxiv.org해당 논문을 보고 작성.. 연구실 공부 2024. 10. 15.
[논문] Avocodo: Generative Adversarial Network for Artifact-free Vocoder https://arxiv.org/abs/2206.13404 Avocodo: Generative Adversarial Network for Artifact-free VocoderNeural 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-quality speech waveforms. Since the perceptually important speech components arearxiv.org해당 논문을 보고 작성했습니다. Abstract빠른 추론 속도와.. 연구실 공부 2024. 10. 10.
[논문] LUKE: Deep Contextualized Entity Representation with Entity-aware Self-attention https://arxiv.org/abs/2010.01057 LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentionEntity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and entitiarxiv.org해당 논문을 보고 작성했습니다. A.. 연구실 공부 2024. 9. 13.
[논문] Simple Unsupervised Keyphrase Extraction using Sentence Embeddings https://arxiv.org/abs/1801.04470 Simple Unsupervised Keyphrase Extraction using Sentence EmbeddingsKeyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and generalizes very poorly outside the domainarxiv.org해당 논문을 보고 작성했습니다. Abstractkeyphras.. 연구실 공부 2024. 9. 10.
[논문] LoCoNet: Long-Short Context Network for Active Speaker Detection https://arxiv.org/abs/2301.08237 LoCoNet: Long-Short Context Network for Active Speaker DetectionActive Speaker Detection (ASD) aims to identify who is speaking in each frame of a video. ASD reasons from audio and visual information from two contexts: long-term intra-speaker context and short-term inter-speaker context. Long-term intra-speaker contextarxiv.org해당 논문을 보고 작성했습니다. AbstractActive Spe.. 연구실 공부 2024. 9. 6.
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