Robust Feature Extraction Using Variable Window Function in Autocorrelation Domain for Speech Recognition

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概要

• 論文の詳細を見る

• This paper presents a new feature extraction method for robust speech recognition based on the autocorrelation mel frequency cepstral coefficients (AMFCCs) and a variable window. While the AMFCC feature extraction method uses the fixed double-dynamic-range (DDR) Hamming window for higher-lag autocorrelation coefficients, which are least affected by noise, the proposed method applies a variable window, depending on the frame energy and periodicity. The performance of the proposed method is verified using an Aurora-2 task, and the results confirm a significantly improved performance under noisy conditions.

• 2009-11-01

著者

• Lee Sangho

  School Of Electrical Engineering And Computer Science Kyungpook National University

• HA Jeonghyun

  School of Electrical Engineering and Computer Science, Kyungpook National University

• HONG Jaekeun

  School of Electrical Engineering and Computer Science, Kyungpook National University

• Hong Jaekeun

  School Of Electrical Engineering And Computer Science Kyungpook National University

• Ha Jeonghyun

  School Of Electrical Engineering And Computer Science Kyungpook National University

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• Robust Feature Extraction Using Variable Window Function in Autocorrelation Domain for Speech Recognition
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