Top

International Journal of Speech Technology

Published in:

28-03-2018

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

Authors: Ismail Shahin, Ali Bou Nassif, Mohammed Bahutair

Published in: International Journal of Speech Technology | Issue 2/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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”.

previous article Speech recognition with reference to Assamese language using novel fusion technique

next article Application of non-negative frequency-weighted energy operator for vowel region detection

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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

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

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

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.CrossRefMATH

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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

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.MathSciNetCrossRef

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.CrossRef

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

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

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.CrossRef

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.CrossRef

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.

Title: Emirati-accented speaker identification in each of neutral and shouted talking environments
Authors: Ismail Shahin
Ali Bou Nassif
Mohammed Bahutair
Publication date: 28-03-2018
Publisher: Springer US
Published in: International Journal of Speech Technology / Issue 2/2018
Print ISSN: 1381-2416
Electronic ISSN: 1572-8110
DOI: https://doi.org/10.1007/s10772-018-9502-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2018

An efficient wavelet-based adaptive filtering algorithm for automatic blind speech enhancement

Adaptive framing based similarity measurement between time warped speech signals using Kalman filter

Chhattisgarhi speech corpus for research and development in automatic speech recognition

Singing voice separation using mono-channel mask

Continuous Punjabi speech recognition model based on Kaldi ASR toolkit

Performance analysis of adaptive variational mode decomposition approach for speech enhancement