@techreport{oai:ipsj.ixsq.nii.ac.jp:00056945,
 author = {荒川, 隆行 and 辻川剛範 and 磯谷亮輔 and Takayuki, ARAKAWA and MasanoriTSUJIKAWA and RyosukeISOTANI},
 issue = {127(2005-SLP-059)},
 month = {Dec},
 note = {雑音の種類に頑健な音声認識手法であるModel Based Wiener Filter法を提案する．本手法は，信号処理的手法であるWiener Filterと，音声GMMによるMMSE推定法を組み合わせたものである．AURORA2－Jタスクを用いて評価を行ったところ，ETSIの提唱するAdvanced Front－Endと較べて単語正解精度が平均値で同程度，雑音の種類によるばらつきでは3分の1程度となり，提案法が雑音の種類に頑健に動作することが確かめられた．, We propose a new approach for noise robust speech recognition, Model-Based Wiener Filter. This method takes three steps to estimate clean speech signals from noisy speech signals. The first step is the spectral subtraction (SS). Since the SS averagely subtracts noise components, the estimated speech signals often include distortion. In the second step, the distortion caused by SS is reduced using the minimum mean square error estimation for a Gaussian mixture model. In the final step, the Wiener Filtering is performed with the decision-directed method. Experiments are conducted using the AURORA2J database. The results show that the proposed method performs as well as the ETSI advanced front-end in average and the variation range of the recognition accuracy according to the kind of noise is about one third, which demonstrates the robustness of the proposed method.},
 title = {Model－Based Wiener Filterによる雑音の種類に頑健な苫声認識},
 year = {2005}
}