Non-linear frequency warping for VTLN using subglottal resonances and the third formant frequency

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Alwan

2014

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Based on our recently-proposed frequency-warping scheme using subglottal resonances (SGRs), this paper addresses two wellknown limitations of conventional vocal-tract length normalization (VTLN): (1) its sub-optimal nature owing to the lack of frequencydependent scaling, and (2) sensitivity to noise. Based on the idea of filter-bank interpolation, a novel approach is proposed to realize the combined effect of VTLN and SGR-based warping (which provides frequency-dependent scaling). Using the Wall Street Journal database and the conventional MFCC front end, SGR warping is shown to be complementary to VTLN in performance. Since SGR warping depends more on the given signal and less on models trained a priori, we argue that SGR warping is less sensitive to noise than VTLN. Through experiments on the AURORA-4 database with power-normalized cepstral coefficients as noise-robust front-end features, we show that SGR warping is better than VTLN, in clean as well as multi-conditional training.

Section: Methodssupporting

confidence: 52%

Section: Methodsmentioning

confidence: 99%

Section: Sgr Warping In Additive Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Frequency warping using subglottal resonances: Complementarity with VTLN and robustness to additive noise

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Alwan

2014

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Estimating Speaker Height and Subglottal Resonances Using MFCCs and GMMs

IEEE Signal Process. Lett.

Alwan

2014

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Abstract-This letter investigates the use of MFCCs and GMMs for 1) improving the state of the art in speaker height estimation, and 2) rapid estimation of subglottal resonances (SGRs) without relying on formant and pitch tracking (unlike our previous algorithm in [1]). The proposed system comprises a set of height-dependent GMMs modeling static and dynamic MFCC features, where each GMM is associated with a height value. Furthermore, since SGRs and height are correlated, each GMM is also associated with a set of SGR values (known a priori). Given a speech sample, speaker height and SGRs are estimated as weighted combinations of the values corresponding to the N most-likely GMMs. We assess the importance of using dynamic MFCC features and the weighted decision rule, and demonstrate the efficacy of our approach via experiments on height estimation (using TIMIT) and SGR estimation (using the Tracheal Resonance database [15]).

Robust speaker identification via fusion of subglottal resonances and cepstral features

Guo

Yang

The Journal of the Acoustical Society of America

et al. 2017

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This letter investigates the use of subglottal resonances (SGRs) for noise-robust speaker identification (SID). It is motivated by the speaker specificity and stationarity of subglottal acoustics, and the development of noise-robust SGR estimation algorithms which are reliable at low signal-to-noise ratios for large datasets. A two-stage framework is proposed which combines the SGRs with different cepstral features. The cepstral features are used in the first stage to reduce the number of target speakers for a test utterance, and then SGRs are used as complementary second-stage features to conduct identification. Experiments with the TIMIT and NIST 2008 databases show that SGRs, when used in conjunction with power-normalized cepstral coefficients and linear prediction cepstral coefficients, can improve the performance significantly (2%–6% absolute accuracy improvement) across all noise conditions in mismatched situations.