Using long-term information to improve robustness in speaker identification
MetadataShow full item record
In this paper we propose two new methods of improving the robustness of Automatic Speaker Identification systems. These methods rely on using long-term information in the speech signal to improve the robustness of the features. The first method involves averaging filterbank parameters from consecutive short-time frames over a longer window. The second method investigates the use of frame lengths longer than generally assumed stationary. We show that these two methods result in an improvement over standard Mel Frequency Cepstral Coefficients in the presence of additive white Gaussian noise in speaker identification applications. Furthermore, additional improvements are observed at mid-range SNR when the proposed methods are used in combination.
4th International Conference on Signal Processing and Communication Systems, ICSPCS'2010. Proceedings
© 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.