基于HMM的MLPC和MFCC噪声语音识别性能评价

2010 13th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2010-12-01 DOI:10.1109/ICCITECHN.2010.5723868

M. Rahman, M. Islam

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引用次数: 6

摘要

本文采用类听觉特征MLPC和MFCC作为前端，在Aurora-2数据库上对基于隐马尔可夫模型(HMM)的噪声语音识别性能进行了评价。干净的数据集用于训练，测试集A用于检查性能。结果表明，MLPC和MFCC的识别性能基本一致，MLPC和MFCC的平均词正确率分别为59.05%和59.21%。对噪声类型的地铁和展览，MLPC比MFCC更有效，而对牙牙声和汽车噪声，MFCC更有优势。

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Performance evaluation of MLPC and MFCC for HMM based noisy speech recognition

In this paper auditory like features MLPC and MFCC have been used as front-end and their performance has been evaluated on Aurora-2 database for Hidden Markov Model (HMM) based noisy speech recognition. The clean data set is used for training and test set A is used to examine the performance. It has been found that almost the same recognition performance has been obtained both for MLPC and MFCC and the average word accuracy for MLPC and for MFCC is found to be 59.05% and 59.21%, respectively. It has also been observed that the MLPC is more effective than MFCC for noise type subway and exhibition, on the other hand, MFCC is more superior for babble and car noises.

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2010 13th International Conference on Computer and Information Technology (ICCIT)

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