1.9k citations · 2.2k across the 59 of their papers we have counts for
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ICASSP 2023 Deep Noise Suppression Challenge
Harishchandra Dubey, Ashkan Aazami, Vishak Gopal +9
Deep Speech Enhancement Challenge is the 5th edition of deep noise suppression (DNS) challenges organized at ICASSP 2023 Signal Processing Grand Challenges. DNS challenges were org…
Real-Time Target Sound Extraction
Bandhav Veluri, Justin Chan, Malek Itani +3
We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture wit…
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
Heming Wang, Yao Qian, Xiaofei Wang +6
Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a…
Continuous Speech Separation with Ad Hoc Microphone Arrays
Dongmei Wang, Takuya Yoshioka, Zhuo Chen +3
Speech separation has been shown effective for multi-talker speech recognition. Under the ad hoc microphone array setup where the array consists of spatially distributed asynchrono…
Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings
Xuankai Chang, Naoyuki Kanda, Yashesh Gaur +3
An end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR) model was proposed recently to jointly perform speaker counting, speech recognition and speaker identif…
Don't shoot butterfly with rifles: Multi-channel Continuous Speech Separation with Early Exit Transformer
Sanyuan Chen, Yu Wu, Zhuo Chen +3
With its strong modeling capacity that comes from a multi-head and multi-layer structure, Transformer is a very powerful model for learning a sequential representation and has been…
Continuous speech separation: dataset and analysis
Zhuo Chen, Takuya Yoshioka, Liang Lu +6
This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior studies on speech separation use pre-segmented signals of artificial…
Cracking the cocktail party problem by multi-beam deep attractor network
Zhuo Chen, Jinyu Li, Xiong Xiao +4
While recent progresses in neural network approaches to single-channel speech separation, or more generally the cocktail party problem, achieved significant improvement, their perf…