Search Results for author:
Found 16 papers, 5 papers with code
Speak in the Scene: Diffusion-based Acoustic Scene Transfer toward Immersive Speech Generation
This paper introduces a novel task in generative speech processing, Acoustic Scene Transfer (AST), which aims to transfer acoustic scenes of speech signals to diverse environments.
MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion
We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in noisy and reverberant acoustic environments.
HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders
This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments.
Imaginary Voice: Face-styled Diffusion Model for Text-to-Speech
This is the first time that face images are used as a condition to train a TTS model.
MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition
Based on the reliability, the activated expert and the language-agnostic expert are aggregated to represent language-conditioned embedding for efficient speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Diffusion-based Generative Speech Source Separation
We propose DiffSep, a new single channel source separation method based on score-matching of a stochastic differential equation (SDE).
Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting
In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences.
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions.
Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement
Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly.
Look Who's Talking: Active Speaker Detection in the Wild
Face tracks are extracted from the videos and active segments are annotated based on the timestamps of VoxConverse in a semi-automatic way.
Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation
In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing.
MIRNet: Learning multiple identities representations in overlapped speech
Many approaches can derive information about a single speaker's identity from the speech by learning to recognize consistent characteristics of acoustic parameters.
End-to-End Lip Synchronisation Based on Pattern Classification
The goal of this work is to synchronise audio and video of a talking face using deep neural network models.
FaceFilter: Audio-visual speech separation using still images
The objective of this paper is to separate a target speaker's speech from a mixture of two speakers using a deep audio-visual speech separation network.
Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision
We build on earlier work to train embeddings that are more discriminative for uni-modal downstream tasks.
Perfect match: Improved cross-modal embeddings for audio-visual synchronisation
This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronization.
