477 citations · 1.7k across the 84 of their papers we have counts for
10 papers · 1 filter
Learning Singing From Speech
Liqiang Zhang, Chengzhu Yu, Heng Lu +5
We propose an algorithm that is capable of synthesizing high quality target speaker's singing voice given only their normal speech samples. The proposed algorithm first integrate s…
PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network
Chengqi Deng, Chengzhu Yu, Heng Lu +2
Singing voice conversion is to convert a singer's voice to another one's voice without changing singing content. Recent work shows that unsupervised singing voice conversion can be…
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
Zhenyu Tang, Lianwu Chen, Bo Wu +2
We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-ba…
End-to-End Multi-Channel Speech Separation
Rongzhi Gu, Jian Wu, Shi-Xiong Zhang +6
The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end…
A comprehensive study of speech separation: spectrogram vs waveform separation
Fahimeh Bahmaninezhad, Jian Wu, Rongzhi Gu +4
Speech separation has been studied widely for single-channel close-talk microphone recordings over the past few years; developed solutions are mostly in frequency-domain. Recently,…
Deep Extractor Network for Target Speaker Recovery From Single Channel Speech Mixtures
Jun Wang, Jie Chen, Dan Su +4
Speaker-aware source separation methods are promising workarounds for major difficulties such as arbitrary source permutation and unknown number of sources. However, it remains cha…
Joint Separation and Denoising of Noisy Multi-talker Speech using Recurrent Neural Networks and Permutation Invariant Training
Morten Kolbæk, Dong Yu, Zheng-Hua Tan +1
In this paper we propose to use utterance-level Permutation Invariant Training (uPIT) for speaker independent multi-talker speech separation and denoising, simultaneously. Specific…
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training
Yanmin Qian, Xuankai Chang, Dong Yu
Although great progresses have been made in automatic speech recognition (ASR), significant performance degradation is still observed when recognizing multi-talker mixed speech. In…
Recognizing Multi-talker Speech with Permutation Invariant Training
Dong Yu, Xuankai Chang, Yanmin Qian
In this paper, we propose a novel technique for direct recognition of multiple speech streams given the single channel of mixed speech, without first separating them. Our technique…
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
Morten Kolbæk, Dong Yu, Zheng-Hua Tan +1
In this paper we propose the utterance-level Permutation Invariant Training (uPIT) technique. uPIT is a practically applicable, end-to-end, deep learning based solution for speaker…