Identification of Age And Gender Using HMM

Deep Neural Network Hidden Markov Models, or DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over Gaussian mixture model based HMMs (GMM-HMMs). The accurate methods to profile different characteristics of a speaker from recorded voice patterns, which facilitate to identify him/her or at least narrow down the number of suspects. Here they propose a new gender and age group recognition approach based on Hidden Markov Model (HMM). First, an acoustic model is trained for all speakers in a training database including male and female speakers of different age. Finally, Supervised HMM is applied to detect the gender and age group of unseen test speakers. Designing a new HMM-based approach for speaker, gender and age estimation, which improves the accuracy of the state-of-the-art speaker age estimation methods with statistical significance. Analyzing the effect of major factors influencing the automatic gender and age estimation systems. 1.INTRODUCTION Recently there has been a growing interest to improve human-computer interaction. It is well-known that, to achieve effective Human-Computer Intelligent Interaction (HCII), computers should be able to interact naturally with the users, i.e. the mentioned interaction should mimic human-human interactions. HCII is becoming really relevant in applications such as smart home, smart office and virtual reality, and it may acquire importance in all aspects of future peoples life[6]. Speech age & gender recognition aims at recognizing the underlying state of the speaker from his or her speech signal[2]. This is mainly motivated by intelligent Human Machine Interaction required for different kinds of applications. In the field of speech age & gender recognition, a number of classification approaches have already been explored. According to the used acoustic features, the recognition models can be classified into two types: 1) for supra segmental prosodic features, such as the mean, median, standard deviation, range, or percentile of short time pitch (energy), estimated over the whole utterance, global models such as Gaussian mixture model(GMM), support vector machine(SVM), artificial neutral networks (ANN) and k-NN have been adopted. 2) for frame based dynamic spectral features like Mel Filter bank Cestrum Coefficient (MFCC), dynamical models such as Hidden Markov Model (HMM) are considered [1], [2]. Compared to the global models, dynamic modelling approaches provide a better consideration of the temporal dynamics of speech[6]. Understanding human states is indispensable for humanhuman interaction and social contact. Human age & gender states affect perception and rational decision making during human-human interactions. Hence, automatic speech recognition is important in harmonious interactions or communication between computers and human beings. The challenging research field, “affective computing,” introduced by Picard [1] aims at enabling computers to recognize, express, and have age & gender. The speech recognition focused only on considering vocal modality. Based on psychological analysis [8], [9] it was found that human states were mainly transferred through multiple channels such as face, voice, body gesture, and speech content. For this reason, exploring data fusion strategies can achieve better recognition performance[12]. Many data fusion approaches have been developed in recent years. Fusion operation can be conducted at the feature level, decision level, and model level for audiovisual speech recognition [9]. In feature-level fusion [10], facial and vocal features are concatenated to construct joint feature vectors and then modeled by a single classifier for speech recognition. However, fusion at the feature level will increase the dimensionality and may suffer from the problem of data sparseness. In terms of decision-level fusion [14], multiple signals can be modeled by the corresponding classifier first and then the recognitions from each classifier are fused in the end. Although fusion at the decision level enables us to interpret the performance of different classifiers and to gain insights into the role of multiple modalities, the assumption of conditional independence does not consider mutual correlation among multiple modalities. Contrary to the decision level, model-level fusion focuses on the mutual correlation among the multiple signal streams, but it is difficult to explore the contributions of multiple modalities. A peculiar and very important developing area concerns the remote monitoring of elderly or ill people. Indeed, due to the increasing aged population, HCII systems able to help live independently are regarded as useful tools. Despite the sign cant advances aimed at supporting elderly citizens, many issues have to be addressed in order to help aged ill people to live independently. In this context recognizing people age & gender state and giving a suitable feedback may play a crucial role. As a consequence, age & gender recognition represents a hot research area in both industry and academically. There is much research in this area and there have been some successful products [10]. Usually, age & gender recognition systems are based on facial or voice features. This is a solution, designed to be employed in a Smart Environment, able to capture the age & gender state of a person starting from a registration of the speech signals in the surrounding obtained by mobile devices such as smart phones. Traditional recommender systems then use these profiles, together with meta-data and ratings from other users in the Bhavana R. Jawale et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1643-1647