Robust speaker identification in babble noise
Mangesh Sudhir Deshpande, Raghunath Sambhaji Holambe · 2011
Performance of speaker recognition systems strongly degrades in the presence of background noise, like the babble noise. Speech babble is one of the most challenging noise interference due to its speaker/speech like characteristics. In contrast to existing works, the aim is to improve noise robustness focusing on the features only. To derive robust features, amplitude modulation - frequency modulation (AM-FM) based speaker model is proposed which combines the speech production and perception mechanism. The performance is evaluated using clean speech corpus from TIMIT database combined with babble noise from the NOISEX-92 database. Experimental results show that the proposed features significantly improve the performance over the conventional Mel frequency cepstral coefficient (MFCC) features under mismatched training and testing environments.