477 citations · 1.7k across the 84 of their papers we have counts for
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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…
Empirical Evaluation of Parallel Training Algorithms on Acoustic Modeling
Wenpeng Li, BinBin Zhang, Lei Xie +1
Deep learning models (DLMs) are state-of-the-art techniques in speech recognition. However, training good DLMs can be time consuming especially for production-size models and corpo…