Name
Title | ||
|---|---|---|
Capturing Complementary Information via Reversed Filter Bank and Parallel Implementation with MFCC for Improved Text-Independent Speaker Identification |
Abstract | ||
|---|---|---|
A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system have been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank, it captures vocal tract characteristics more effectively in the lower frequency regions. This work proposes a new set of features using a complementary filter bank structure which improves distinguishability of speaker specific cues present in the higher frequency zone. Unlike high level features that are difficult to extract, the proposed feature set involves little computational burden during the extraction process. When combined with MFCC via a parallel implementation of speaker models, the proposed feature improves performance baseline of MFCC based system. The proposition is validated by experiments conducted on two different kinds of databases namely YOHO (microphone speech) and POLYCOST (telephone speech) with two different classifier paradigms, namely Gaussian Mixture Models (GMM) and Polynomial Classifier (PC) and for various model orders. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICCTA.2007.35 | Kolkata |
Keywords | Field | DocType |
speaker model,speaker modeling scheme,human auditory system,speaker specific cue,robust feature extraction unit,high level feature,improved text-independent speaker identification,parallel implementation,capturing complementary information,si application,proposed feature set,proposed feature,new set,reversed filter bank,feature extraction,filter bank,vocal tract,speaker recognition,gaussian mixture models,mfcc,audio signal processing,robustness,loudspeakers,gaussian mixture model,speech,mel frequency cepstral coefficients,mel frequency cepstral coefficient | Mel-frequency cepstrum,Pattern recognition,Computer science,Filter bank,Feature extraction,Speech recognition,Speaker recognition,Artificial intelligence,Audio signal processing,Classifier (linguistics),Mixture model,Vocal tract | Conference |
ISBN | Citations | PageRank |
0-7695-2770-1 | 5 | 0.46 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandipan Chakroborty | 1 | 31 | 3.34 |
| Anindya Roy | 2 | 119 | 12.62 |
| Sourav Majumdar | 3 | 5 | 0.46 |
| Goutam Saha | 4 | 255 | 23.17 |