Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

Abstract

Speech signals are being used as a primary input source in human–computer interaction (HCI) to develop several applications, such as automatic speech recognition (ASR), speech emotion recognition (SER), gender, and age recognition. Classifying speakers according to their age and gender is a challenging task in speech processing owing to the disability of the current methods of extracting salient high-level speech features and classification models. To address these problems, we introduce a novel end-to-end age and gender recognition convolutional neural network (CNN) with a specially designed multi-attention module (MAM) from speech signals. Our proposed model uses MAM to extract spatial and temporal salient features from the input data effectively. The MAM mechanism uses a rectangular shape filter as a kernel in convolution layers and comprises two separate time and frequency attention mechanisms. The time attention branch learns to detect temporal cues, whereas the frequency attention module extracts the most relevant features to the target by focusing on the spatial frequency features. The combination of the two extracted spatial and temporal features complements one another and provide high performance in terms of age and gender classification. The proposed age and gender classification system was tested using the Common Voice and locally developed Korean speech recognition datasets. Our suggested model achieved 96%, 73%, and 76% accuracy scores for gender, age, and age-gender classification, respectively, using the Common Voice dataset. The Korean speech recognition dataset results were 97%, 97%, and 90% for gender, age, and age-gender recognition, respectively. The prediction performance of our proposed model, which was obtained in the experiments, demonstrated the superiority and robustness of the tasks regarding age, gender, and age-gender recognition from speech signals.

Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms

“…In the traditional non-automatic method, the age estimation formula or trend has been obtained separately because males and females have different progression of tooth and bone development and aging processes 11 , 27 , 28 . However, using data augmentation and a CNN, age and gender can be automatically distinguished beyond that achievable by a human observer 26 , 29 , 30 . Technological innovation in artificial intelligence has led to an era in which it is no longer necessary to divide the dataset by gender, and it is possible to easily estimate the age and gender of unidentified persons.…”

Emotional Speech Recognition Method Based on Word Transcription

“…Today, the speech emotion recognition system (SER) assesses the emotional state of the speaker by examining his/her speech signal [24][25][26]. Work [27] proposes key technologies for recognition of speech emotions based on neural networks and recognition of facial emotions based on SVM, and in paper [28], they show a system of emotion recognition based on an artificial neural network (ANN) and its comparison with a system based on the scheme Hidden Markov Modeling (HMM). Both systems were built on the basis of probabilistic pattern recognition and acoustic phonetic modeling approaches.…”

Intelligent Deep Learning Enabled Crowd Detection and Classification Model in Real Time Surveillance Videos

“…In recent times, security surveillance system has employed visual-based tracking and detection technologies for enhancing safety and convenience for human beings [1]. Human tracking and detection systems are important topics in a surveillance scheme.…”