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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD)
An International Open Access Journal |   ISSN: 2456-4184 |  IMPACT FACTOR: 5.57

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Paper Title: Age Estimation and Gender Recognition by Speech Analysis using Neural Network
Authors Name: Ashish Dongre , Kushbu Undirwade , Monika Kumbhare , Nandeshwar , Prof. Mohammad Ali
Author Reg. ID:
IJNRD_180041
Published Paper Id: IJNRD1804006
Published In: Volume 3 Issue 4, April-2018
Abstract: Automatic age and gender recognition for speech applications is very important for a number of reasons. One of the reasons is that it can improve human-machine interaction. For example, the advertisements can be specialized based on the age and the gender of the person on the phone. It also can help identify suspects in criminal cases or at least it can minimize the number of suspects. Some other uses of this system can be applied for adaptation of waiting queue music where a different type of music can be played according to the person’s age and gender. And also using this age and gender recognition system, the statistics about age and gender information for a specific population can be learned. To remove the noise and to get the features of speech examples, some digital signal processing techniques were used. Useful speech features that were used in this work were: pitch frequency and cepstral representations. The performance of the age and gender recognition system depends on the speech features used. As the first speech feature, the fundamental frequency was selected. Fundamental frequency is the main differentiating factor between male and female speakers. Also, fundamental frequency for each age group is different. So in order to build age and gender recognition system, fundamental frequency was used. To get the fundamental frequency of speakers, harmonic to sub harmonic ratio method was used. The speech was divided into frames and fundamental frequency for each frame was calculated. In order to get the fundamental frequency of the speaker, the mean value of all the speech frames were taken. It turns out that, fundamental frequency is not only a good discriminator gender, but also it is a good discriminator of age groups simply because there is a distinction between age groups and the fundamental frequencies
Keywords: short-time average magnitude, short-time energy, short-time zero crossing rate, short-time auto-correlation
Cite Article: "Age Estimation and Gender Recognition by Speech Analysis using Neural Network", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.3, Issue 4, page no.41-44, April-2018, Available :http://www.ijnrd.org/papers/IJNRD1804006.pdf
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