34 citations · 195 across the 59 of their papers we have counts for
15 papers · 1 filter
Language-specific Acoustic Boundary Learning for Mandarin-English Code-switching Speech Recognition
Zhiyun Fan, Linhao Dong, Chen Shen +4
Code-switching speech recognition (CSSR) transcribes speech that switches between multiple languages or dialects within a single sentence. The main challenge in this task is that d…
Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation
Jiawei Huang, Yi Ren, Rongjie Huang +7
Large diffusion models have been successful in text-to-audio (T2A) synthesis tasks, but they often suffer from common issues such as semantic misalignment and poor temporal consist…
StyleS2ST: Zero-shot Style Transfer for Direct Speech-to-speech Translation
Kun Song, Yi Ren, Yi Lei +5
Direct speech-to-speech translation (S2ST) has gradually become popular as it has many advantages compared with cascade S2ST. However, current research mainly focuses on the accura…
ByteCover3: Accurate Cover Song Identification on Short Queries
Xingjian Du, Zijie Wang, Xia Liang +3
Deep learning based methods have become a paradigm for cover song identification (CSI) in recent years, where the ByteCover systems have achieved state-of-the-art results on all th…
Token-level Speaker Change Detection Using Speaker Difference and Speech Content via Continuous Integrate-and-fire
Zhiyun Fan, Zhenlin Liang, Linhao Dong +6
In multi-talker scenarios such as meetings and conversations, speech processing systems are usually required to segment the audio and then transcribe each segmentation. These two s…
Sequence-level Speaker Change Detection with Difference-based Continuous Integrate-and-fire
Zhiyun Fan, Linhao Dong, Meng Cai +2
Speaker change detection is an important task in multi-party interactions such as meetings and conversations. In this paper, we address the speaker change detection task from the p…
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
Chen Shen, Yi Liu, Wenzhi Fan +6
This paper describes our submission to ICASSP 2022 Multi-channel Multi-party Meeting Transcription (M2MeT) Challenge. For Track 1, we propose several approaches to empower the clus…
HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection
Ke Chen, Xingjian Du, Bilei Zhu +3
Audio classification is an important task of mapping audio samples into their corresponding labels. Recently, the transformer model with self-attention mechanisms has been adopted…
Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data
Ke Chen, Xingjian Du, Bilei Zhu +3
Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of a…
Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding
Chao Wang, Zhonghao Li, Benlai Tang +4
Recently, phonetic posteriorgrams (PPGs) based methods have been quite popular in non-parallel singing voice conversion systems. However, due to the lack of acoustic information in…
Attention-based cross-modal fusion for audio-visual voice activity detection in musical video streams
Yuanbo Hou, Zhesong Yu, Xia Liang +4
Many previous audio-visual voice-related works focus on speech, ignoring the singing voice in the growing number of musical video streams on the Internet. For processing diverse mu…
Rule-embedded network for audio-visual voice activity detection in live musical video streams
Yuanbo Hou, Yi Deng, Bilei Zhu +2
Detecting anchor's voice in live musical streams is an important preprocessing for music and speech signal processing. Existing approaches to voice activity detection (VAD) primari…
PPG-based singing voice conversion with adversarial representation learning
Zhonghao Li, Benlai Tang, Xiang Yin +4
Singing voice conversion (SVC) aims to convert the voice of one singer to that of other singers while keeping the singing content and melody. On top of recent voice conversion work…
Contrastive Unsupervised Learning for Audio Fingerprinting
Zhesong Yu, Xingjian Du, Bilei Zhu +1
The rise of video-sharing platforms has attracted more and more people to shoot videos and upload them to the Internet. These videos mostly contain a carefully-edited background au…
ByteCover: Cover Song Identification via Multi-Loss Training
Xingjian Du, Zhesong Yu, Bilei Zhu +2
We present in this paper ByteCover, which is a new feature learning method for cover song identification (CSI). ByteCover is built based on the classical ResNet model, and two majo…