Air- and bone-conductive integrated microphones for robust speech detection and enhancement

By Yanli Zheng, Zicheng Liu, Zhengyou Zhang, M. Sinclair, Jasha Droppo, Li Deng, A. Acero, Xuedong Huang. 2003

90 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pharmacy, 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 Artificial Intelligence

1. Li Deng
2. Dong Yu
3. Jasha Droppo
4. A. Acero
5. Zicheng Liu
6. Jinyu Li
7. Xuedong Huang
8. Jianfeng Gao

In Signal Processing

1. Li Deng
2. Dong Yu
3. Jasha Droppo
4. A. Acero
5. Zhengyou Zhang
6. Jinyu Li
7. Xuedong Huang
8. Yu Tsao

Citations in the corpus

• Multi-sensory microphones for robust speech detection, enhancement and recognition (2004, background): “But it is so obvious from the white curve. Details can be found in [ 8 ].”
• Multi-sensory speech processing: incorporating automatically extracted hidden dynamic information (2005, methodology): “In [4], we proposed an algorithm based on the SPLICE technique to learn the mapping between the two streams and the clean speech signal.”
• 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).”
• Challenges in adopting speech recognition (2004, background): “Further improvement on system robustness can be achieved by using additional sensory information ([11] and see Turk’s article in this section).”