Shout detection in noise

For the task of detecting shouted speech in a noisy environment, this paper introduces a system based on mel frequency cepstral coefficient (MFCC) feature extraction, unsupervised frame dropping and Gaussian mixture model (GMM) classification. The evaluation material consists of phonemically identical speech and shouting as well as environmental noise of varying levels. The performance of the shout detection system is analyzed by varying the MFCC feature extraction with respect to 1) the feature vector length and 2) the spectrum estimation method. As for feature vector length, the best performance is obtained using 30 MFCC coefficients, which is more than what is conventionally used. In spectrum estimation, a scheme that combines a linear prediction spectrum envelope with spectral fine structure outperforms the conventional FFT.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Jouni Pohjalainen, Paavo Alku, Tomi Kinnunen
• Keywords: Feature Vector Length, ICASSP 2011, MFCC Feature Extraction, Signal Processing, Spectrum Estimation