Multi-sensory microphones for robust speech detection, enhancement and recognition

By Zhengyou Zhang, Zicheng Liu, M. Sinclair, A. Acero, Li Deng, Jasha Droppo, Xuedong Huang, Yanli Zheng. 2004

72 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Jinyu Li
2. John R. Hershey
3. Reinhold Häb-Umbach
4. Li Deng
5. Yu Tsao
6. Chiou-Shann Fuh
7. A. Subramanya
8. Yifan Gong

In Signal Processing

1. Jinyu Li
2. Dong Yu
3. Yifan Gong
4. Li Deng
5. John R. Hershey
6. Deliang Wang
7. Shinji Watanabe
8. Yu Tsao

Citations in the corpus

• Multi-sensory speech processing: incorporating automatically extracted hidden dynamic information (2005, methodology): “In [4, 5], a novel hardware solution was developed and described to combat against highly nonstationary acoustic noise such as background interfering speech.”
• Bone-conducted speech enhancement using deep denoising autoencoder (2018, methodology): “Meanwhile, many approaches have been proposed to combine ACM and BCM using a single sound capture device (Zheng et al., 2003; Liu et al., 2004; Zhang et al., 2004).”
• An Overview of Noise-Robust Automatic Speech Recognition (2014, background): “Some speci fi c techniques pertaining to the above topics not covered in this article can be found in [39] for multi-sensory speech detection, in [40], [41] for nonstationary noise estimation and ASR robustness against nonstationary noise, in [42] for a new multichannel framework for speech source…”
• Model-based fusion of bone and air sensors for speech enhancement and robust speech recognition (2004, methodology): “Although we cannot make a strict comparison for lack of a standardized dataset, this results compare favorably with prior art in [1], and [2].”