14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

Impact of Noise Reduction and Spectrum Estimation on Noise Robust Speaker Identification

Keith W. Godin, Seyed Omid Sadjadi, John H. L. Hansen

University of Texas at Dallas, USA

Many spectrum estimation methods and speech enhancement algorithms have previously been evaluated for noise-robust speaker identification (SID). However, these techniques have mostly been evaluated over artificially noised, mismatched training tasks with GMM-UBM speaker models. It is therefore unclear whether performance improvements observed with these methods translate to a broader range of noisy SID tasks. This study compares selected spectrum estimation methods from three classes: cochlear filterbanks, alternative time-domain windowing, and linear predictionbased techniques, as well as a set of frequency-domain noise reduction algorithms, across a suite of 8 evaluation tasks. The evaluation tasks are designed to expand upon the limited tasks addressed in past evaluations by exploring three research questions: performance on real noise versus artificial noise, performance on matched training tasks versus mismatched tasks, and performance when paired with an i-vector backend versus a GMM-UBM backend. We find that noise-robust spectrum estimation methods can improve the performance of SID systems over the range of noise tasks evaluated, including real noisy tasks, matched training tasks, and i-vector backends. However, performance on the typical GMM-UBM mismatched artificially noised case did not predict performance on other tasks. Finally, the matched enrollment case is a significantly different problem than the mismatched enrollment case.

Full Paper

Bibliographic reference.  Godin, Keith W. / Sadjadi, Seyed Omid / Hansen, John H. L. (2013): "Impact of noise reduction and spectrum estimation on noise robust speaker identification", In INTERSPEECH-2013, 3656-3660.