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[논문] Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion https://arxiv.org/abs/2005.07025 Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice ConversionEmotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotional voice conversion are mostly carried out under the assumption that emotioarxiv.org해당 논문을 보고 작성했습니다... 공감수 0 댓글수 0 2025. 2. 19.
[논문] Identity Conversion for Emotional Speakers: A Study for Disentanglement of Emotion Style and Speaker Identity https://www.researchgate.net/publication/355496277_Identity_Conversion_for_Emotional_Speakers_A_Study_for_Disentanglement_of_Emotion_Style_and_Speaker_Identity해당 논문을 보고 작성했습니다. Abstractexpressive voice conversion은 speaker identity와 speaker-dependent emotion style을 동시에 변환합니다. speech emotion의 계층적 구조 때문에, speaker-dependent emotional style을 분리하는 것은 어렵습니다. variational autoencoder (VAE)를 이용한 speaker d.. 공감수 0 댓글수 0 2025. 2. 17.
[논문] Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation https://arxiv.org/abs/2204.10020 Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data AugmentData augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are available. Although the quality of VC is crucial for this approac.. 공감수 0 댓글수 0 2025. 2. 15.
[논문] Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing https://arxiv.org/abs/2302.10536 Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain PPrimary goal of an emotional voice conversion (EVC) system is to convert the emotion of a given speech signal from one style to another style without modifying the linguistic content of the signal. Most of the state-of-the-art approaches.. 공감수 0 댓글수 0 2025. 2. 14.
[논문] Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering https://ieeexplore.ieee.org/document/8114355 Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse FilteringDue to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional automatic speech recognition (ASR) systems trained on neutral speech degrades significantly when whisper is applied. In order tieeexplore.iee.. 공감수 0 댓글수 0 2025. 2. 13.
[논문] SEF-VC: Speaker Embedding Free Zero-shot Voice Conversion with Cross Attention https://arxiv.org/abs/2312.08676 SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross AttentionZero-shot voice conversion (VC) aims to transfer the source speaker timbre to arbitrary unseen target speaker timbre, while keeping the linguistic content unchanged. Although the voice of generated speech can be controlled by providing the speaker embeddinarxiv.org해당 논문을 보고 작성했습니다. Abst.. 공감수 0 댓글수 0 2025. 2. 4.
[논문] Learning Arousal-Valence Representation From Categorical Emotion Labels of Speech https://ieeexplore.ieee.org/document/10445890 Learning Arousal-Valence Representation from Categorical Emotion Labels of SpeechDimensional representations of speech emotions such as the arousal-valence (AV) representation provide a continuous and fine-grained description and control than their categorical counterparts. They have wide applications in tasks such as dynamic emotion uieeexplore.ieee.. 공감수 0 댓글수 0 2025. 1. 19.
[논문] StethoSpeech: Speech Generation Through a Clinical Stethoscope Attached to the Skin https://dl.acm.org/doi/10.1145/3678515 StethoSpeech: Speech Generation Through a Clinical Stethoscope Attached to the Skin | Proceedings of the ACM on Interactive, MobWe introduce StethoSpeech, a silent speech interface that transforms flesh-conducted vibrations behind the ear into speech. This innovation is designed to improve social interactions for those with voice disorders, and furthermore .. 공감수 2 댓글수 0 2025. 1. 16.
[논문] Speech Resynthesis from Discrete Disentangled Self-Supervised Representations https://arxiv.org/abs/2104.00355 Speech Resynthesis from Discrete Disentangled Self-Supervised RepresentationsWe propose using self-supervised discrete representations for the task of speech resynthesis. To generate disentangled representation, we separately extract low-bitrate representations for speech content, prosodic information, and speaker identity. This alarxiv.org해당 논문을 보고 작성했습니다. Abstr.. 공감수 1 댓글수 0 2025. 1. 15.
[논문] Recovering Implicit Pitch Contours from Formants in Whispered Speech https://arxiv.org/abs/2307.03168 Recovering implicit pitch contours from formants in whispered speechWhispered speech is characterised by a noise-like excitation that results in the lack of fundamental frequency. Considering that prosodic phenomena such as intonation are perceived through f0 variation, the perception of whispered prosody is relatively difarxiv.org해당 논문을 보고 작성했습니다. Abstractwhispe.. 공감수 0 댓글수 0 2025. 1. 8.
[논문] A Fast and Accurate Pitch Estimation Algorithm Based on the Pseudo Wigner-Ville Distribution https://arxiv.org/abs/2210.15272 A Fast and Accurate Pitch Estimation Algorithm Based on the Pseudo Wigner-Ville DistributionEstimation of fundamental frequency (F0) in voiced segments of speech signals, also known as pitch tracking, plays a crucial role in pitch synchronous speech analysis, speech synthesis, and speech manipulation. In this paper, we capitalize on the high timearxiv.org해당 논문을 보.. 공감수 1 댓글수 0 2025. 1. 2.
[논문] CinC-GAN for Effective $F_0$ prediction for Whisper-to-Normal Speech Conversion https://arxiv.org/abs/2008.07788 CinC-GAN for Effective F0 prediction for Whisper-to-Normal Speech ConversionRecently, Generative Adversarial Networks (GAN)-based methods have shown remarkable performance for the Voice Conversion and WHiSPer-to-normal SPeeCH (WHSP2SPCH) conversion. One of the key challenges in WHSP2SPCH conversion is the prediction of fundamentalarxiv.org해당 논문을 보고 작성했습니다. Abstra.. 공감수 0 댓글수 0 2024. 12. 31.
[논문] Analysing Diffusion-based Generative Approaches Versus Discriminative Approaches for Speech Restoration [2211.02397] Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech RestorationDiffusion-based generative models have had a high impact on the computer vision and speech processing communities these past years. Besides data generation tasks, they have also .. 공감수 0 댓글수 0 2024. 12. 30.
[논문] Self-Supervised Learning for Speech Enhancement Through Synthesis https://arxiv.org/abs/2211.02542 Self-Supervised Learning for Speech Enhancement through SynthesisModern speech enhancement (SE) networks typically implement noise suppression through time-frequency masking, latent representation masking, or discriminative signal prediction. In contrast, some recent works explore SE via generative speech synthesis, whearxiv.org해당 논문을 보고 작성했습니다. Abstract현대 speech.. 공감수 0 댓글수 0 2024. 12. 30.
[논문] MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames https://arxiv.org/abs/2102.12841 Mask CycleGAN: Unpaired Multi-modal Domain Translation with Interpretable Latent VariableWe propose Mask CycleGAN, a novel architecture for unpaired image domain translation built based on CycleGAN, with an aim to address two issues: 1) unimodality in image translation and 2) lack of interpretability of latent variables. Our innovation in thearxiv.org해당 논문을 보고 작성.. 공감수 0 댓글수 0 2024. 12. 27.
[논문] GESPER: A Unified Framework for General Speech Restoration https://ieeexplore.ieee.org/abstract/document/10095557 Gesper: A Unified Framework for General Speech RestorationThis paper describes the legends-tencent team’s real-time General Speech Restoration (Gesper) system submitted to the ICASSP 2023 Speech Signal Improvement (SSI) Challenge. This newly proposed system is a two-stage architecture, in which the speech restoieeexplore.ieee.org해당 논문을 보고 작성.. 공감수 0 댓글수 0 2024. 12. 26.
[논문] SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesis Approach Using Channel Modeling https://arxiv.org/abs/2203.12937 SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesis Approach Using Channel ModelingWe present a self-supervised speech restoration method without paired speech corpora. Because the previous general speech restoration method uses artificial paired data created by applying various distortions to high-quality speech corpora, it cannot suffiar.. 공감수 2 댓글수 0 2024. 12. 24.
[논문] Seen and Unseen Emotional Style Transfer for Voice Conversion with a New Emotional Speech Dataset https://arxiv.org/abs/2010.14794 Seen and Unseen emotional style transfer for voice conversion with a new emotional speech datasetEmotional voice conversion aims to transform emotional prosody in speech while preserving the linguistic content and speaker identity. Prior studies show that it is possible to disentangle emotional prosody using an encoder-decoder network conditioned on darxiv.org해당 .. 공감수 3 댓글수 0 2024. 12. 20.
[논문] Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model https://arxiv.org/abs/2405.01730 Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion ModelExpressive voice conversion (VC) conducts speaker identity conversion for emotional speakers by jointly converting speaker identity and emotional style. Emotional style modeling for arbitrary speakers in expressive VC has not been extensively explored. Prearxiv.org.. 공감수 3 댓글수 0 2024. 12. 18.
[논문] Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity https://arxiv.org/abs/2407.14800 Towards Realistic Emotional Voice Conversion using Controllable Emotional IntensityRealistic emotional voice conversion (EVC) aims to enhance emotional diversity of converted audios, making the synthesized voices more authentic and natural. To this end, we propose Emotional Intensity-aware Network (EINet), dynamically adjusting intonatioarxiv.org해당 논문을 보고 작성했습니다... 공감수 0 댓글수 0 2024. 12. 17.
[논문] VoiceMixer: Adversarial Voice Style Mixup https://proceedings.neurips.cc/paper/2021/hash/0266e33d3f546cb5436a10798e657d97-Abstract.html VoiceMixer: Adversarial Voice Style MixupRequests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with their co-authors prior to requeproceed.. 공감수 1 댓글수 0 2024. 12. 16.
[논문] Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-shot Speaker Adaptation https://diff-hiervc.github.io/audio_demo/ Diff-HierVC DemoAblation study Results of ablation study on zero-shot VC tasks with unseen speakers from VCTK dataset. For all methods, the number of sampling iterations is 6.diff-hiervc.github.io해당 논문을 보고 작성했습니다. Abstractvoice conversion (VC) system은 voice style을 잘 변환하지만, 여전히 부정확한 pitch와 낮은 speaker adaptation quality 문제를 가지고 있습니다. 이러한 문제를 해결하기 위해, 저자들은 .. 공감수 0 댓글수 0 2024. 12. 12.
[논문] Towards Improving NAM-to-Speech Synthesis Intelligibility using Self-Supervised Speech Models https://arxiv.org/abs/2407.18541 Towards Improving NAM-to-Speech Synthesis Intelligibility using Self-Supervised Speech ModelsWe propose a novel approach to significantly improve the intelligibility in the Non-Audible Murmur (NAM)-to-speech conversion task, leveraging self-supervision and sequence-to-sequence (Seq2Seq) learning techniques. Unlike conventional methods that expliciarxiv.org해당 논문을 .. 공감수 0 댓글수 0 2024. 12. 6.
[논문] Improving Zero-Shot Voice Style Transfer Via Disentangled Representation Learning https://openreview.net/forum?id=TgSVWXw22FQ Improving Zero-Shot Voice Style Transfer via Disentangled...Voice style transfer, also called voice conversion, seeks to modify one speaker's voice to generate speech as if it came from another (target) speaker. Previous works have made progress on voice...openreview.net해당 논문을 보고 작성했습니다. Abstractvoice conversion이라고도 불리는 voice style transfer는 한 speaker의.. 공감수 1 댓글수 0 2024. 11. 20.
[논문] PAVITS: Exploring Prosody-Aware VITS for End-to-End Emotional Voice Conversion https://ieeexplore.ieee.org/document/10446191 PAVITS: Exploring Prosody-Aware VITS for End-to-End Emotional Voice ConversionIn this paper, we propose Prosody-aware VITS (PAVITS) for emotional voice conversion (EVC), aiming to achieve two major objectives of EVC: high content naturalness and high emotional naturalness, which are crucial for meeting the demands of human perceptioieeexplore.ieee.or.. 공감수 1 댓글수 0 2024. 11. 15.
[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.. 공감수 7 댓글수 0 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.. 공감수 2 댓글수 0 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.. 공감수 5 댓글수 0 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.. 공감수 5 댓글수 0 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.. 공감수 1 댓글수 0 2024. 10. 10.

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