高斯混和模型運用在語音、聲響辨識系統方展成熟，然而在高度的環境背景雜訊，其辨識效果會大幅下降。本論文提出結合短時間與長時間的特徵萃取向量，提昇環境在高度背景雜訊下的辨識率。短時間特徵向量採用梅爾倒頻譜係數(Mel-frequency cepstral coefficients, MFCCs)，長時間特徵係數採用調制頻譜向量(Modulation spectral vectors, MSVs)，調制頻譜特徵向量可以萃取訊號在頻率域的能量包絡，能量包絡的特性能夠有效的抵抗環境雜訊的干擾。
為了讓系統對於雜訊更加強健，本論文提一種訓練方式，在訓練的過程中，高斯混合模型就先看過含有雜訊的資料，這種方式有助於提升在低訊雜比的訊號辨識率。本論文進行辨識8種類別的室內環境聲響事件，在訊雜比 0 dB 的環境下，辨識率達八成以上。

The Gaussian mixture model (GMM) has developed well both in the speech and sound recognition, but it does not perform well in the high background noisy environment. This thesis proposes a method combining short-term and long-term features to overcome this issue. Here the short-term features are Mel-frequency cepstral coefficients (MFCCs) and the long-term features are the modulation spectral vectors (MSVs) calculated in the frequency domain. The MSVs contains the envelope message of signals which is a good feature against high noise.
For robustness against noise, this thesis proposes a method to learn noisy data while training on GMMs. This method could raise the recognition accuracy in the low singal-to-noise ratio (SNR) case. The method was evaluated on a database which consists of 8 different indoor sound event classes. It achieves > 80 % accuracy at 0 dB SNR.

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