Noise Robust Speaker Identification: Using Nonlinear Modeling Techniques

Session variability is one of the challenging tasks in forensic speaker identification. This variability in terms of mismatched environments seriously degrades the identification performance. In order to address the problem of environment mismatch due to noise, different types of robust features are discussed in this chapter. In state-of-the art features, the speech production system is modeled as a linear source-filter model. However, this modeling technique neglects some nonlinear aspects of speech production, which carry some speaker-specific information. Furthermore, the state-of-the art features are based on either speech production mechanism or speech perception mechanism. To overcome such limitations of existing features, features derived using non-linear modeling techniques are proposed in the chapter. The proposed features, Teager energy operator based cepstral coefficients (TEOCC) and amplitude-frequency modulation (AM-FM) based ‘Q’ features show significant improvement in speaker identification rate in mismatched environments. The performance of these features is evaluated for different types of noise signals in the NOISEX-92 database with clean training and noisy testing environments. The speaker identification rate achieved is 57% using TEOCC features and 97% using AM-FM based ‘Q’ features for 0 dB SNR compared to 25.5% using MFCC features, when the signal is corrupted by car engine noise. It is shown that, with the proposed features, speaker identification accuracy can be increased in presence of noise, without any additional pre-processing of the signal to remove noise.

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