Cepstral parameter compensation for HMM recognition in noise
Speech Communication - Special issue on speech processing in adverse conditions
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
On stochastic feature and model compensation approaches to robust speech recognition
Speech Communication - Special issue on robust speech recognition
Babble noise: modeling, analysis, and applications
IEEE Transactions on Audio, Speech, and Language Processing
Multicomponent AM–FM Representations: An Asymptotically Exact Approach
IEEE Transactions on Audio, Speech, and Language Processing
Speaker Identification Using Instantaneous Frequencies
IEEE Transactions on Audio, Speech, and Language Processing
Optimization of temporal filters for constructing robust features in speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Robust Speaker Recognition in Noisy Conditions
IEEE Transactions on Audio, Speech, and Language Processing
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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.