Gender recognition using neural networks and ASR techniques

• Autorzy: Sas J. , Sas A.
• Języki publikacji: EN

Abstrakty

EN

The paper presents the simple technique of speaker gender recognition that uses MFCC features typically applied in automatic speech recognition. Artificial neural network is used as a classifier. The speech signal is first divided into 20 ms frames. For each frame, Mel-Frequency Cepstral Coefficients are extracted and the created feature vector is provided into a neural network classifier, which individually classifies each frame as male or female sample. Finally, the whole utterance is classified by selecting the class, for which the sum of corresponding neural network outputs is greater. The advantage of the method is that it can be easily combined with speech recognition, because both processes (gender recognition and speech recognition) are based on the same features. This way, no additional logic and no extra computational power is needed to extract features necessary for gender recognition. The method was experimentally evaluated using speech samples in English and in Polish. The comparison with other methods described in literature based on other feature extraction methods shows the superiority of the proposed approach, especially in cases where the recognition is carried out in noisy environment or using poor audio equipment.

Słowa kluczowe

EN

artificial neural networks; speech recognition; gender recognition

PL

sztuczne sieci neuronowe; rozpoznawanie mowy; rozpoznawanie płci

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2013
• Tom: Vol. 22
• Strony: 179--187
• Opis fizyczny: Bibliogr. 12 poz., tab., wykr.

Twórcy

autor

Sas J.

• Institute of Informatics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland

autor

Sas A.

• Institute of Computer Science, University of Wroclaw, Joliot-Curie 15, 50-383 Wroclaw, Poland.

Bibliografia

• [1] DEIV S., BHATTACHARAYA M., Automatic Gender Identification for Hindi Speech Recognition, International Journal of Computer Applications, New York, 2011, Vol. 31, No. 5, pp. 1-8.
• [2] HU Y., WU D., NUCCI A., Pitch-based Gender Identification with Two-stage Classification. Security and Communication Networks, New York, 2012, Vol. 5, Issue 2, pp. 211-225.
• [3] HUANG F., LEE T., Pitch Estimation in Noisy Speech Using Accumulated Peak Spectrum and Sparse Estimation Technique. IEEE Transactions on Audio, Speech and Language Processing, 2013, Vol. 21, No. 1, pp. 99-109.
• [4] KARWAN J., SAEED K., A New Algorithm for Speech and Gender Recognition on the Basis of Voiced Parts of Speech. In: N. Chaki and A. Cortesi (Eds.): CISIM 2011, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 113-120.
• [5] KUNJITHAPATHAM M. et al., Gender Classification in Speech Recognition using Fuzzy Logic and Neural Network. The International Arab Journal of Information Technology, September 2013, Vol. 10, No. 5, pp. 477-485.
• [6] MAKHOUL J., Linear Prediction: A Tutorial Review. Proceedings of the IEEE, 1975, Vol. 63, No. 4, pp. 561-589.
• [7] MOHD I., SHAHIN A., International Journal of Speech Technology, Springer, Berlin, Heidelberg, 2013, Vol. 16, pp. 133-141.
• [8] PARKER R., Real-Time Kinect Player Gender Recognition using Speech Analysis, Internet publication, (http://www.radfordparker.com/papers/gender.pdf).
• [9] RAKESH K., DUTTA S., SHAMA K., Gender recognition using speech processing techniques in LabView. International Journal of Advances in Engineering & Technology, 2011, Vol. 1, Issue 2, pp. 51-63.
• [10] TING H., YINCHUN Y., ZHAOHUI W., Combining MFCC and Pitch to Enhance the Performance of the Gender Recognition. Proceedings of 8th Int. Conf, on Signal Processing ICSP-2006, Benjing, 2006, Vol. 1 (IEEE digital publication).
• [11] WISNIEWSKI M., KUNISZYN-JOZWIAK W., Automatic detection and classification of phoneme repetitions using HTK toolkit. Journal of Medical Informatics and Technologies, 2011, Vol. 17, pp. 141-148.
• [12] YOUNG S., EVERMAN G., The HTK Book (for HTK Version 3.4), Cambridge University Engineering Department, 2009.