Voice source characterization using pitch synchronous discrete cosine transform for speaker identification

Abstract

A characterization of the voice source (VS) signal by the pitch synchronous (PS) discrete cosine transform (DCT) is proposed. With the integrated linear prediction residual (ILPR) as the VS estimate, the PS DCT of the ILPR is evaluated as a feature vector for speaker identification (SID). On TIMIT and YOHO databases, using a Gaussian mixture model (GMM)-based classifier, it performs on par with existing VS-based features. On the NIST 2003 database, fusion with a GMM-based classifier using MFCC features improves the identification accuracy by 12% in absolute terms, proving that the proposed characterization has good promise as a feature for SID studies.

Exploration of Compressed ILPR Features for Replay Attack Detection

“…CILPR is computed from the ILPR based voice source representation and captures the temporal shape of voice source signal between two GCIs. This feature has also been explored for speaker identification in [14]. ILPR is estimated by passing a non pre-emphasized version of speech signal through an LP inverse filter, the LP coefficients of the inverse filters are obtained from the corresponding pre-emphasized speech signal.…”

Pitch tracking algorithm based on evolutionary computing with regularisation in very low SNR

“…Although pitch detection has been studied for decades, and a lot of achievements have obtained, it is still challenging to estimate pitch from a speech in the presence of strong noise. Actually, many applications are in a complicate and severe noise environment, so studying the pitch detection is meaningful for realisation technology of speech processing in very low signal‐to‐noise ratio (SNR), especially under the SNR of − 5 dB [1–4].…”

Exploring different attributes of source information for speaker verification with limited test data

“…DCTILPR captures the glottal shape information of a speaker in a pitch synchronous manner. 11,12 But this does not capture the periodicity information of the signal denoting how much periodic it is. MPDSS feature is a variant of spectral flatness measure, and captures the periodicity information as the peak to dip ratio of the spectrum of a signal measures the periodicity.…”

“…11,12 An epoch extraction algorithm is applied, and using these epochs, a voiced/unvoiced decision based on maximum normalized cross-correlation is applied as in Refs. 17 and 18.…”

“…Also, as the excitation source features are less dependent on the amount of phonetic content, the duration of enrollment/testing can be less. 9,10 In earlier work, 11,12 a source feature called discrete cosine transform of the integrated linear prediction residual (DCTILPR) is computed. The score level fusion of this feature with MFCC feature was an improvement over the baseline system using only MFCC under limited test data conditions.…”