Age can be inferred from the speech signal by inferring various factors such as physical attributes of voice, linguistic attributes, frequency, speech rate, etc., This paper discusses on extracting the spectral features of speech such as Cepstral Coefficients, Spectral Decrease, Centroid, Flatness, Spectral Entropy,Jitter and Shimmer as inputs which would also helps in classifying speaker age through deep learning techniques.A novel approach is addressed along with the model for implementation using Deep Neural Network and Convolutional Neural Network for classifying the features using three different classifiers.The results obtained from the proposed system would outline the performance in speaker age recognition. With the advent of conversational voice recognition systems such as Alexa, SIRI, OK Google, etc., natural language conversational scheme including Chatbot and voice recognition systems are in new high and determining the age of a speaker is critical for setting the pertinent context. Com-parable gains in ASR performance were observed when con-sidering two larger age ranges (60-75 and 76-90) instead of six five-year ranges, suggesting that it would be sufficient to use the two large ranges only.

Compared with a base-line ASR system with AMs trained using young adult speech and a WER of 13.9%, the selection of five-year-range adapted AMs, based on the estimated age of the speakers, led to a de-crease in WER of about 9.3% relative (1.3% absolute). Age estimation (AE) based on i-vectors and support vector re-gression achieved mean error rates of about 4.2 and 4.5 years for males and females, respectively. A large corpus of read speech from European Portuguese speakers aged 60 or over was used. In this study, we investigated automatic age estimation in order to se-lect age-specific adapted AMs. Ageing is known to al-ter speech production in ways that require ASR systems to be adapted, in particular at the level of acoustic modeling. Using such models, ASR performance may decrease by about 10% abso-lute when transcribing elderly speech. Phone-like acoustic models (AMs) used in large-vocabulary automatic speech recognition (ASR) systems are usually trained with speech collected from young adult speakers.

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