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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Differences in counting skills between Chinese and German children are accompanied by differences in processing of approximate numerical magnitude information Lonnemann, Jan; Li, Su; Zhao, Pei... In: Frontiers in psychology 9 (2019), 8 S. (2019) BASE Show details 2 Differences in Counting Skills Between Chinese and German Children Are Accompanied by Differences in Processing of Approximate Numerical Magnitude Information Lonnemann, Jan (Prof. Dr.); Li, Su; Zhao, Pei. - 2019 BASE Show details 3 Differences in Counting Skills Between Chinese and German Children Are Accompanied by Differences in Processing of Approximate Numerical Magnitude Information Lonnemann, Jan; Li, Su; Zhao, Pei. - : Frontiers Media S.A., 2019 BASE Show details 4 Do Preschool Children Learn to Read Words from Environmental Prints? Zhao, Jing; Zhao, Pei; Weng, Xuchu. - : Public Library of Science, 2014 BASE Show details 5 CASA Based Speech Separation for Robust Speech Recognition Han Runqiang; Zhao Pei; Gao Qin; Zhang Zhiping; Wu Hao; Wu Xihong In: http://www.dcs.shef.ac.uk/~martin/SpeechSeparationChallenge/han.pdf Abstract: This paper introduces a speech separation system which is used as a front-end processing step for automatic speech recognition (ASR). It is designed based on computational auditory scene analysis (CASA) to separate the target speech from the interference speech. Firstly, the mixed speech is preprocessed based on auditory peripheral model. Secondly, pitch tracking is conducted and the dominant pitch is used as a main cue to find the target speech. Thirdly, the time frequency (TF) units are merged into many segments, which are combined into streams in CASA initial grouping step. And then a regrouping strategy is employed to refine those streams via amplitude modulate (AM) cues, which are finally organized by the speaker recognition techniques into corresponding speakers. At last, the output streams will be reconstructed to compensate the missing data in the abovementioned processing steps by a cluster based feature reconstruction. The experimental results of ASR show that to the speech with high TMR (>-6dB), the system does not perform well, and to the low TMR (<-6dB) the recognition accuracy of processed speech is higher than that of unprocessed speech. 1. URL: http://www.dcs.shef.ac.uk/~martin/SpeechSeparationChallenge/han.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.6579 BASE Hide details

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