Tiruchirappalli, India
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Meena K.,Bharathidhasan University | Subramaniam K.R.,Shrimathi Indira Gandhi College | Gomathy M.,Shrimathi Indira Gandhi College
European Journal of Scientific Research | Year: 2011

Gender grouping is one of the most important processes in speech recognition. Generally gender grouping is done by considering some parameters in the speech. Among them the most important parameter is frequency. Normally the frequency of females is higher than that of the males. By considering this condition the gender grouping is done. In some males the frequency is very high and in some females the frequency is very low. In such situation we didn't get the exact result. So, here we propose a gender grouping method which considers three features and uses the Euclidean distance method to calculate the distance between the features. For that first a common threshold value is calculated from the training dataset and during testing if we give a set of speech signal as input, the output we obtained is grouping of speech signals based on gender. The implementation result shows the performance of our proposed technique in gender grouping. © EuroJournals Publishing, Inc. 2011.


Gomathy M.,Shrimathi Indira Gandhi College | Meena K.,Bharathidhasan University | Meena K.,Shrimathi Indira Gandhi College | Subramaniam K.R.,Shrimathi Indira Gandhi College
International Journal of Speech Technology | Year: 2011

One of the most important processes in speech processing is gender classification. Generally gender classification is done by considering pitch as feature. In general the pitch value of female is higher than the male. In some cases, pitch value of male is higher and female is low, in that cases this classification will not obtain the exact result. By considering this drawback here proposed a gender classification method which considers three features and uses fuzzy logic and neural network to identify the given speech signal belongs to which gender. For training fuzzy logic and neural network, training dataset is generated by considering the above three features. After completion of training, a speech signal is given as input, fuzzy and neural network gives an output, for that output mean value is taken and this value gives the speech signal belongs to which gender. The result shows the performance of our method in gender classification. © 2011 Springer Science+Business Media, LLC.


Meena K.,Bharathidhasan University | Subramaniam K.R.,Shrimathi Indira Gandhi College | Gomathy M.,Shrimathi Indira Gandhi College
International Journal of Signal and Imaging Systems Engineering | Year: 2014

Gender classification is one of the most important processes in speech processing. Generally gender classification is done by considering pitch as feature. Normally the pitch value of female is higher than the male. By using this condition, gender classification process takes place. But in some case the pitch value of male is higher and also pitch of female is low, in that case this classification does not provide the exact result. By considering the abovementioned drawback, here proposed a new method for gender classification in speech processing which considers three features and uses fuzzy logic and neural network to identify the gender of the speaker. The features considered in the proposed method is energy entropy, short time energy and zero crossing rate. For training fuzzy logic and neural network, training dataset is generated using the above three features. Then mean value is computed from the result obtained from fuzzy logic and neural network. The gender classification is done by using this mean value. The implementation result shows the performance of the proposed technique in gender classification. © 2014 Inderscience Enterprises Ltd.


Meena K.,Bharathidhasan University | Subramaniam K.,Shrimathi Indira Gandhi College | Gomathy M.,Shrimathi Indira Gandhi College
International Arab Journal of Information Technology | Year: 2013

Nowadays classification of gender is one of the most important processes in speech processing. Usually gender classification is based on considering pitch as feature. The pitch value of female is higher than the male. In most of the recent research works gender classification process is performed using the abovementioned condition. In some cases the pitch value of male is higher and also pitch of some female is low, in that case this classification does not produce the exact required result. By considering the aforementioned problem we have here proposed a new method for gender classification method which considers three features. The new method uses fuzzy logic and neural network to identify the gender of the speaker. To train fuzzy logic and neural network, training dataset is generated by using the above three features. Then mean value is calculated for the obtained result from fuzzy logic and neural network. By using this threshold value, the proposed method identifies the speaker belongs to which gender. The implementation result shows the performance of the proposed technique in gender classification.

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