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Comparing Noise Compensation Methods for Robust Prediction of Acoustic Speech Features From Mfcc Vectors in Noise

机译：比较噪声补偿方法以从噪声中的Mfcc向量稳健地预测语音特征

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Almajai Ibrahim; Darch Jonathan; Milner Ben; Vaseghi Saeed;
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年度 2008
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1. Noise speech recognition based on robust features and a model-based noise compensation evaluated on aurora-2 task [J] . Kaisheng Yao, Jingdong Chen, Kuldip K. Paliwal, 電子情報通信学会技術研究報告. 音声. Speech . 2001,第522期

机译：基于Aurora-2任务评估的基于鲁棒功能的噪声语音识别和基于模型的噪声补偿
2. Noise speech recognition based on robust features and a model-based noise compensation evaluated on aurora-2 task [J] . Kaisheng Yao, Jingdong Chen, Kuldip K. Paliwal, 電子情報通信学会技術研究報告. 言語理解とコミュニケーション. Natural Language Understanding and Models of Communication . 2001,第520期

机译：基于Aurora-2任务评估的基于鲁棒功能的噪声语音识别和基于模型的噪声补偿
3. Noise speech recognition based on robust features and a model-based noise compensation evaluated on aurora-2 task [J] . Kaisheng Yao, Jingdong Chen, Kuldip K. Paliwal, 電子情報通信学会技術研究報告. 言語理解とコミュニケーション. Natural Language Understanding and Models of Communication . 2001,第520期

机译：基于鲁棒特征的噪声语音识别和Aurora-2任务评估基于模型的噪声补偿
4. COMPARING NOISE COMPENSATION METHODS FOR ROBUST PREDICTION OF ACOUSTIC SPEECH FEATURES FROM MFCC VECTORS IN NOISE [C] . Ben Milner, Jonathan Darch, Ibrahim Almajai, European Signal Processing Conference . 2009

机译：比较MFCC矢量噪声稳健预测的噪声补偿方法
5. Compensation for Nonlinear Distortion in Noise for Robust Speech Recognition. [D] . Harvilla, Mark J. 2014

机译：噪声中的非线性失真补偿，用于鲁棒的语音识别。
6. Speech-in-Noise Test results of compensation claimants for noise induced hearing loss in Korean male workers: Words-in-Noise Test (WIN) and quick-Hearing-in-Noise Test (HINT) [O] . Ji Soo Kim, Joong Keun Kwon, Nam Jeong Kim, 2021

机译：韩国男性工人噪声引起的噪声诱导损失的噪音索赔人的语音测试结果：单词 - 噪声测试（WIN）和快速听音 - 噪音测试（提示）
7. Noise Robust Automatic Speech Recognition Method for the Robot with Motor Noise using Missing Feature Theory [O] . Yoshitaka Nishimura, Mitsuru Ishizuka, Kazuhiro Nakadai, 2007

机译：使用缺失特征理论具有电机噪声机器人的噪声鲁棒自动语音识别方法

Comparing Noise Compensation Methods for Robust Prediction of Acoustic Speech Features From Mfcc Vectors in Noise

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