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Emirati-accented speaker identification in each of neutral and shouted talking environments

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Abstract

This work is devoted to capturing Emirati-accented speech database (Arabic United Arab Emirates database) in each of neutral and shouted talking environments in order to study and enhance text-independent Emirati-accented “speaker identification performance in shouted environment” based on each of “first-order circular suprasegmental hidden Markov models (CSPHMM1s), second-order circular suprasegmental hidden Markov models (CSPHMM2s), and third-order circular suprasegmental hidden Markov models (CSPHMM3s)” as classifiers. In this research, our database was collected from 50 Emirati native speakers (25 per gender) uttering eight common Emirati sentences in each of neutral and shouted talking environments. The extracted features of our collected database are called “Mel-Frequency Cepstral Coefficients (MFCCs)”. Our results show that average Emirati-accented speaker identification performance in neutral environment is 94.0, 95.2, and 95.9% based on CSPHMM1s, CSPHMM2s, and CSPHMM3s, respectively. On the other hand, the average performance in shouted environment is 51.3, 55.5, and 59.3% based, respectively, on “CSPHMM1s, CSPHMM2s, and CSPHMM3s”. The achieved “average speaker identification performance in shouted environment based on CSPHMM3s” is very similar to that obtained in “subjective assessment by human listeners”.

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References

  • Al-Dahri, S. S., Al-Jassar, Y. H., Alotaibi, Y. A., Alsulaiman, M. M., & Abdullah-Al-Mamun, K. A. (2008). A word-dependent automatic Arabic speaker identification system. In signal processing and information technology (ISSPIT 2008) (pp. 198–202).

  • Campbell, W. M., Campbell, J. R., Reynolds, D. A., Singer, E., & Torres-Carrasquillo, P. A. (2006). Support vector machines for speaker and language recognition. Computer Speech and Language, 20, 210–229.

    Article  Google Scholar 

  • Casale, S., Russo, A., & Serano, S. (2007). Multistyle classification of speech under stress using feature subset selection based on genetic algorithms. Speech Communication, 49(10), 801–810.

    Article  Google Scholar 

  • Falk, T. H., & Chan, W. Y. (2010). Modulation spectral features for robust far-field speaker identification. IEEE Transactions on Audio, Speech and Language Processing, 18(1), 90–100.

    Article  Google Scholar 

  • Farrell, K. R., Mammone, R. J., & Assaleh, K. T. (1994). Speaker recognition using neural networks and conventional classifiers. IEEE Transactions on Speech and Audio Processing, 2, 194–205.

    Article  Google Scholar 

  • Furui, S. (1991). Speaker-dependent-feature-extraction, recognition and processing techniques. Speech Communication, 10, 505–520.

    Article  Google Scholar 

  • Grozdić, I. T., Jovičić, S. T., & Subotić, M. (2017). Whispered speech recognition using deep denoising autoencoder. Engineering Applications of Artificial Intelligence, 59, 15–22.

    Article  Google Scholar 

  • Hong, Q. Y., & Kwong, S. (2005). A genetic classification method for speaker recognition. Engineering Applications of Artificial Intelligence, 18, 13–19.

    Article  Google Scholar 

  • https://catalog.ldc.upenn.edu/LDC2002S02 (West Point Arabic Speech).

  • https://catalog.ldc.upenn.edu/LDC2006S43 (Gulf Arabic Conversational Telephone Speech)

  • Kinnunen, T., Karpov, E., & Franti, P. (2006). Real-time speaker identification and verification. IEEE Transactions on Audio, Speech and Language Processing, 14(1), 277–288.

    Article  MATH  Google Scholar 

  • Kinnunen, T., & Li, H. (2010) An overview of text-independent speaker recognition: From features to supervectors. Speech Communication, 52(1), 12–40.

    Article  Google Scholar 

  • Kirchhoff, K., Bilmes, J., Das, S., Duta, N., Egan, M., Ji, G., He, F., Henderson, J., Liu, D., Noamany, M., Schone, P., Schwartz, R., Vergyri, D. (2003) Novel approaches to Arabic speech recognition: Report from the 2002 Johns-Hopkins workshop. In proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP) (vol. 1, 2003, pp. 344–347).

  • Krobba, A., Debyeche, M., Amrouche, A. (2010) Evaluation of speaker identification system using GSMEFR speech data, Proc. 2010 International Conference on Design & Technology of Integrated Systems in Nanoscale Era, Hammamet, March 2010, pp. 1–5.

  • Mahmood, A., Alsulaiman, M., & Muhammad, G. (2014) Automatic speaker recognition using multi directional local features (MDLF). Arabian Journal for Science and Engineering, 39(5), 3799–3811.

    Article  Google Scholar 

  • Pavel, M., Ondrej, G., Ondrej, N., Oldrich, P., Frantisek, G., Lukas, B., & Jan, H. C. (2016). Analysis of DNN approaches to speaker identification. In International conference on acoustics, speech and signal processing 2016 (pp. 5100–5104).

  • Polzin, T. S., & Waibel, A. H. (1998). Detecting emotions in speech, cooperative multimodal communication. In 2nd International Conference 1998, CMC, 1998.

  • Reynolds, D. A. (1995) Speaker identification and verification using Gaussian mixture speaker models. Speech Communication, 17(1–2), 91–108.

    Article  Google Scholar 

  • Reynolds, D. A., & Rose, R. C. (1995). Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing, 3(1), 72–83.

    Article  Google Scholar 

  • Saeed, K., & Nammous, M. K. (2007). A speech-and-speaker identification system: Feature extraction, description, and classification of speech signal image. IEEE Transactions on Industrial Electrons, 54(2), 887–897.

    Article  Google Scholar 

  • Shahin, I. (2011). Identifying speakers using their emotion cues. International Journal of Speech Technology, 14(2), 89–98. https://doi.org/10.1007/s10772-011-9089-1.

    Article  Google Scholar 

  • Shahin, I. (2016). Emirati speaker verification based on HMM1s, HMM2s, and HMM3s. In 13th International Conference on Signal Processing (ICSP 2016), Chengdu, China, November 2016, pp. 562–567, https://doi.org/10.1109/ICSP.2016.7877896.

  • Shahin, I. (2006). Enhancing speaker identification performance under the shouted talking condition using second-order circular hidden Markov models. Speech Communication, 48(8), 1047–1055.

    Article  Google Scholar 

  • Shahin, I. (2010). Employing second-order circular suprasegmental hidden Markov models to enhance speaker identification performance in shouted talking environments. EURASIP Journal on Audio, Speech, and Music Processing, 2010(1), 862138. https://doi.org/10.1155/2010/862138.

    Article  Google Scholar 

  • Shahin, I. (2012). Studying and enhancing talking condition recognition in stressful and emotional talking environments based on HMMs, CHMM2s and SPHMMs. Journal on Multimodal User Interfaces, 6(1), 59–71. https://doi.org/10.1007/s12193-011-0082-4.

    Article  Google Scholar 

  • Shahin, I. (2013). Gender-dependent emotion recognition based on HMMs and SPHMMs. International Journal of Speech Technology, 16(2), 133–141. https://doi.org/10.1007/s10772-012-9170-4.

    Article  Google Scholar 

  • Shahin, I. (2014). Novel third-order hidden Markov models for speaker identification in shouted talking environments. Engineering Applications of Artificial Intelligence, 35, 316–323. https://doi.org/10.1016/j.engappai.2014.07.006.

    Article  Google Scholar 

  • Shahin, I. (2016). Employing emotion cues to verify speakers in emotional talking environments. Journal of Intelligent Systems, Special Issue on Intelligent Healthcare Systems, 25(1), 3–17.

    MathSciNet  Google Scholar 

  • Shahin, I. (2016) Speaker identification in a shouted talking environment based on novel third-order circular suprasegmental hidden Markov models. Circuits, Systems and Signal Processing, 35(10), 3770–3792. https://doi.org/10.1007/s00034-015-0220-4.

    Article  MathSciNet  Google Scholar 

  • Shahin, I., & Ba-Hutair, M. N. (2015). Talking condition recognition in stressful and emotional talking environments based on CSPHMM2s. International Journal of Speech Technology, 18(1), 77–90. https://doi.org/10.1007/s10772-014-9251-7.

    Article  Google Scholar 

  • Shahin, I. (2008). Speaker identification in the shouted environment using suprasegmental hidden Markov models. Signal Processing Journal, 88(11), 2700–2708.

    Article  MATH  Google Scholar 

  • Shahin, I. (2013). Speaker identification in emotional talking environments based on CSPHMM2s. Engineering Applications of Artificial Intelligence, 26(7), 1652–1659.

    Article  Google Scholar 

  • Shahin, I., & Ba-Hutair, M. N. (2014). Emarati speaker identification. In 12th International Conference on Signal Processing (ICSP 2014) (pp. 488–493). HangZhou, China.

  • Shahin, I., & Botros, N., Modeling and analyzing the vocal tract under normal and stressful talking conditions. In IEEE SOUTHEASTCON 2001., Clemson, March 2001, pp. 213–220.

  • Staroniewicz, P., & Majewski, W. (2004) SVM based text-dependent speaker identification for large set of voices. In 12th European Signal Processing Conference, EUSIPCO 2004, Vienna, Austria, September 2004, pp. 333–336.

  • Tolba, H. (2011). A high-performance text-independent speaker identification of Arabic speakers using a CHMM-based approach. Alexandria Engineering, 50, 43–47.

    Article  Google Scholar 

  • Zhao, X., Shao, Y., & Wang, D. (2012). CASA-based robust speaker identification. IEEE Transactions on Audio, Speech and Language Processing Journal, 20(5), 1608–1616.

    Article  Google Scholar 

  • Zheng, C., & Yuan, B. Z. (1988). Text-dependent speaker identification using circular hidden Markov models. In IEEE International Conference on Acoustics, Speech and Signal Processing, S13.3, pp. 580–582.

Download references

Acknowledgements

The authors of this work wish to thank University of Sharjah for funding their work through the competitive research project entitled “Capturing, Studying, and Analyzing Arabic Emirati-Accented Speech Database in Stressful and Emotional Talking Environments for Different Applications”, No. 1602040349-P. The authors wish also to thank engineers Merah Al Suwaidi, Deema Al Rais, and Hannah Saud for capturing the Emirati-accented speech database.

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  1. Department of Electrical and Computer Engineering, University of Sharjah, P. O. Box 27272, Sharjah, United Arab Emirates

    Ismail Shahin, Ali Bou Nassif & Mohammed Bahutair

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  1. Ismail Shahin
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  2. Ali Bou Nassif
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  3. Mohammed Bahutair
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Correspondence to Ismail Shahin.

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Shahin, I., Nassif, A.B. & Bahutair, M. Emirati-accented speaker identification in each of neutral and shouted talking environments. Int J Speech Technol 21, 265–278 (2018). https://doi.org/10.1007/s10772-018-9502-0

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