Combining Spatial Clustering with LSTM Speech Models for Multichannel Speech Enhancement

no code implementations • 2 Dec 2020 • Felix Grezes, Zhaoheng Ni, Viet Anh Trinh, Michael Mandel

The system is compared to several baselines on the CHiME3 dataset in terms of speech quality predicted by the PESQ algorithm and word error rate of a recognizer trained on mis-matched conditions, in order to focus on generalization.

Clustering Speech Enhancement

Enhancement of Spatial Clustering-Based Time-Frequency Masks using LSTM Neural Networks

no code implementations • 2 Dec 2020 • Felix Grezes, Zhaoheng Ni, Viet Anh Trinh, Michael Mandel

By using LSTMs to enhance spatial clustering based time-frequency masks, we achieve both the signal modeling performance of multiple single-channel LSTM-DNN speech enhancers and the signal separation performance and generality of multi-channel spatial clustering.

Clustering Speech Enhancement

CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings

no code implementations • 20 Apr 2020 • Shinji Watanabe, Michael Mandel, Jon Barker, Emmanuel Vincent, Ashish Arora, Xuankai Chang, Sanjeev Khudanpur, Vimal Manohar, Daniel Povey, Desh Raj, David Snyder, Aswin Shanmugam Subramanian, Jan Trmal, Bar Ben Yair, Christoph Boeddeker, Zhaoheng Ni, Yusuke Fujita, Shota Horiguchi, Naoyuki Kanda, Takuya Yoshioka, Neville Ryant

Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).

speaker-diarization Speaker Diarization +4