Statistical properties of the warped discrete cosine transform cepstrum compared with MFCC
Rangarao Muralishankar, Abhijeet Sangwan, Douglas D. O’Shaughnessy · 2005
Abstract In this paper, we continue our investigation of the warpeddiscrete cosine transform cepstrum (WDCTC), which wasearlier introduced as a new speech processing feature [1].Here, we study the statistical properties of the WDCTC andcompare them with the mel-frequency cepstral coefficients(MFCC). We report some interesting properties of the WD-CTC when compared to the MFCC: its statistical distrib-ution is more Gaussian-like with lower variance, it obtainsbettervowel clusterseparability,it formstightervowelclus-ters and generates better codebooks. Further, we employthe WDCTC and MFCC features in a 5-vowel recognitiontask using Vector Quantization (VQ) and 1-Nearest Neigh-bour(1-NN)as classifiers. Inourexperiments,the WDCTCconsistently outperforms the MFCC. 1. Introduction We recently introduced the warped discrete cosine trans-form cepstrum (WDCTC) as a new speech processing fea-ture and demonstrated its better performance than the mel-frequency cepstral coefficients (MFCC) in a vowel recog-nition and speaker-identification task [1]. The WDCTC hasshown good promise as a speech processing feature and weare encouraged to further investigate the WDCTC featureand its statistical properties.Alargevolumeoftrainingdataisrequiredto buildspea-ker-independentspeechrecognitionsystems. Onetechniqueof reducing the data size is clustering the data and choos-ing a reasonable number of representative feature vectorsto form codebooks [2]. Hence, codebook techniques arevery relevant and practical to speech recognition systems.We form WDCTC and MFCC codebooks using a k-meansclustering algorithm and compare the codebook statisticsfor clean and noisy vowels using the coefficient of varianceand overlap ratio (defined later). Our experiment demon-strates that the WDCTC codebooks represent the underly-ing vowel data better than MFCC.In order to compare the classification capability of thefeatures, the WDCTC and MFCC are employed in a 5-vowel recognition task. Vector quantization (VQ) and 1-nearestneighbor(1-NN,[2])are usedas classifiersandtheirrecognition performance is reported. We also investigatethe clean and noisy vowel clusters formed by WDCTC andMFCC features and present the average separability of thevowel classes.