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Model Kompanzasyonlu Birinci Derece İstatistikleri ile i-vektörlerin Gürbüzlüğünün Artırılması

Year 2023, Volume: 23 Issue: 1, 123 - 137, 01.03.2023
https://doi.org/10.35414/akufemubid.1134945

Abstract

Konuşmacı tanıma sistemleri özellikle i-vektörlerin performansı sebebiyle son on yılda önemli gelişmeler elde etmiştir. Bu gelişmelere rağmen eğitim ve test verileri arasındaki uyumsuzluk tanıma performansını önemli ölçüde etkilemektedir. Bu çalışmada, model kompanzasyon yöntemleri i-vektör çıkarımı şemasına eklenerek toplanabilir gürültülere karşı gürbüzlüğü artıracak bir çözüm sunulmaktadır. Durağan gürültüler için model kompanzasyon teknikleri oldukça gürbüz sistemler üretir. Paralel Model Kompanzasyonu ve Vektör Taylor Serileri en gelişmiş model kompanzasyon tekniklerinden kabul edilmektedir. Bu metotlar birinci dereceden istatistiklere uygulanarak toplanabilir gürültülerden kaynaklanan uyumsuzluğu azaltacak gürültülü tüm değişkenlik uzayı eğitimi amaçlanmıştır. Tüm değişkenlik matrisin eğitimi, i-vektör boyutunun azaltılması, i-vektörlerin puanlanması gibi geleneksel i-vektör şemasının diğer tüm parçaları değişmeden kalmaktadır. Önerilen yöntem, 6 dB’lik adımlarla -6 dB’den 18 dB’ye kadar çeşitli sinyal-gürültü oranlarına (SNR) sahip dört farklı gürültü tipi ile test edilmiştir. Her iki yöntemle de en düşük SNR seviyelerinde bile eşit hata oranlarında yüksek azalmalar elde edilmiştir. Önerilen yaklaşım eşik hata oranında ortalama olarak %50’den fazla göreceli azalma sağlamıştır.

References

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Increasing the Robustness of i-vectors with Model Compensated First Order Statistics

Year 2023, Volume: 23 Issue: 1, 123 - 137, 01.03.2023
https://doi.org/10.35414/akufemubid.1134945

Abstract

Speaker recognition systems achieved significant improvements over the last decade, especially due to the performance of the i-vectors. Despite the achievements, mismatch between training and test data affects the recognition performance considerably. In this paper, a solution is offered to increase robustness against additive noises by inserting model compensation techniques within the i-vector extraction scheme. For stationary noises, the model compensation techniques produce highly robust systems. Parallel Model Compensation and Vector Taylor Series are considered as state-of-the-art model compensation techniques. Applying these methods to the first order statistics, a noisy total variability space training is aimed, which will reduce the mismatch resulted by additive noises. All other parts of the conventional i-vector scheme remain unchanged, such as total variability matrix training, reducing the i-vector dimensionality, scoring the i-vectors. The proposed method was tested with four different noise types with several signal to noise ratios (SNR) from -6 dB to 18 dB with 6 dB steps. High reductions in equal error rates were achieved with both methods, even at the lowest SNR levels. On average, the proposed approach produced more than 50% relative reduction in equal error rate.

References

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  • Ghosh, P. K., Tsiartas, A., Narayanan, S. 2011. Robust Voice Activity Detection Using Long-Term Signal Variability. IEEE Transactions on Audio, Speech, and Language Processing, 19(3), 600–613.
  • Gong, Y. 2002. A COMPARATIVE STUDY OF APPROXIMATIONS FOR PARALLEL MODEL COMBINATION OF STATIC AND DYNAMIC PARAMETERS. In 7th International Conference on Spoken Language Processing (pp. 1–4). Denver, Colorado, USA.
  • Guo, J., Xu, N., Qian, K., Shi, Y., Xu, K., Wu, Y., Alwan, A. 2018. Deep neural network based i-vector mapping for speaker verification using short utterances. Speech Communication, 105, 92–102.
  • Jinyu, L., Li D., Dong, Y., Yifan, G., Acero, A. 2007. High-performance hmm adaptation with joint compensation of additive and convolutive distortions via Vector Taylor Series. In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU) (pp. 65–70). Kyoto, Japan.
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  • Kalinli, O., Seltzer, M. L., Acero, A. 2009. Noise adaptive training using a vector taylor series approach for noise robust automatic speech recognition. In 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 3825–3828). Taipei, Taiwan.
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  • Kenny, P., Boulianne, G., Ouellet, P., Dumouchel, P. (2007). Speaker and Session Variability in GMM-Based Speaker Verification. IEEE Transactions on Audio, Speech and Language Processing, 15(4), 1448–1460.
  • Kheder, W. Ben, Matrouf, D., Ajili, M., Bonastre, J.-F. 2018. A Unified Joint Model to Deal With Nuisance Variabilities in the i-Vector Space. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(3), 633–645.
  • Kim, W., Hansen, J.H.L. 2009. Feature compensation in the cepstral domain employing model combination. Speech Communication, 51(2), 83–96.
  • Kinnunen, T., Li, H. 2010. An overview of text-independent speaker recognition: From features to supervectors. Speech Communication, 52(1), 12–40.
  • Krobba, A., Debyeche, M., Selouani, S.-A. 2019. Multitaper chirp group delay Hilbert envelope coefficients for robust speaker verification. Multimedia Tools and Applications, 78(14), 19525–19542.
  • Lei, Y., Burget, L., Ferrer, L., Graciarena, M., Scheffer, N. 2012. Towards noise-robust speaker recognition using probabilistic linear discriminant analysis. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4253–4256). Kyoto, Japan.
  • Lei, Y., Burget, L., Scheffer, N. 2013. A noise robust i-vector extractor using vector taylor series for speaker recognition. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 6788–6791). Vancouver, BC, Canada.
  • Lei, Y., McLaren, M., Ferrer, L., Scheffer, N. 2014. Simplified VTS-based I-vector extraction in noise-robust speaker recognition. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4037–4041). Florence, Italy.
  • Lei, Y., Scheffer, N., Ferrer, L., McLaren, M. 2014. A novel scheme for speaker recognition using a phonetically-aware deep neural network. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1695–1699). Florence, Italy.
  • Li, M., Narayanan, S. 2014. Simplified supervised i-vector modeling with application to robust and efficient language identification and speaker verification. Computer Speech and Language, 28(4), 940–958.
  • Li, N., Mak, M.W. 2015) SNR-Invariant PLDA Modeling in Nonparametric Subspace for Robust Speaker Verification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(10), 1648–1659. 7
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There are 66 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Articles
Authors

Gökay Dişken 0000-0002-8680-0636

Zekeriya Tüfekci 0000-0001-7835-2741

Early Pub Date March 1, 2023
Publication Date March 1, 2023
Submission Date June 24, 2022
Published in Issue Year 2023 Volume: 23 Issue: 1

Cite

APA Dişken, G., & Tüfekci, Z. (2023). Increasing the Robustness of i-vectors with Model Compensated First Order Statistics. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 23(1), 123-137. https://doi.org/10.35414/akufemubid.1134945
AMA Dişken G, Tüfekci Z. Increasing the Robustness of i-vectors with Model Compensated First Order Statistics. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. March 2023;23(1):123-137. doi:10.35414/akufemubid.1134945
Chicago Dişken, Gökay, and Zekeriya Tüfekci. “Increasing the Robustness of I-Vectors With Model Compensated First Order Statistics”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 23, no. 1 (March 2023): 123-37. https://doi.org/10.35414/akufemubid.1134945.
EndNote Dişken G, Tüfekci Z (March 1, 2023) Increasing the Robustness of i-vectors with Model Compensated First Order Statistics. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 23 1 123–137.
IEEE G. Dişken and Z. Tüfekci, “Increasing the Robustness of i-vectors with Model Compensated First Order Statistics”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 23, no. 1, pp. 123–137, 2023, doi: 10.35414/akufemubid.1134945.
ISNAD Dişken, Gökay - Tüfekci, Zekeriya. “Increasing the Robustness of I-Vectors With Model Compensated First Order Statistics”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 23/1 (March 2023), 123-137. https://doi.org/10.35414/akufemubid.1134945.
JAMA Dişken G, Tüfekci Z. Increasing the Robustness of i-vectors with Model Compensated First Order Statistics. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2023;23:123–137.
MLA Dişken, Gökay and Zekeriya Tüfekci. “Increasing the Robustness of I-Vectors With Model Compensated First Order Statistics”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 23, no. 1, 2023, pp. 123-37, doi:10.35414/akufemubid.1134945.
Vancouver Dişken G, Tüfekci Z. Increasing the Robustness of i-vectors with Model Compensated First Order Statistics. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2023;23(1):123-37.