An effective gender recognition approach using voice data via deeper LSTM networks

Gender and nationality information has not been exploited in large-scale speaker recognition despite being provided in the popular VoxCeleb1 dataset. This paper explores methods that combine high-level features extracted from the gender and nationality information with low-level acoustic features for speaker identification. To our knowledge, this is the first time that the gender and nationality information provided in VoxCeleb1 is utilized in speaker identification. Specifically, we propose Gender-Guided Spectrogram-Attention network and Nationality-Guided Spectrogram-Attention network that embed gender and nationality information into the spectrogram features, respectively. The resulting gender and nationality embeddings are then used with the spectrogram features together for classification. Experimental results show that the proposed methods can successfully capture the gender and nationality information of the speakers, and can effectively improve speaker identification accuracy. 

Attention based gender and nationality information exploration for speaker identification

Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive backpropagation algorithm had an accuracy of 98% and the Bagging algorithm had an accuracy of 98.10% in the gender identification data. Bagging has the best accuracy among all algorithms, with 55.39% accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%. 

Age and Gender Classification Using Backpropagation and燘agging燗lgorithms

Speaker recognition is the process of extracting speaker-specific details from voice waves to validate the features asserted by system users; in other words, it allows voice-controlled access to a range of services. The research initiates with extraction features from voice signals and employing those features in Artificial Neural Network (ANN) for speaker recognition. Increasing the number of hidden layers and their associated neurons reduces the training error and increases the computational process's complexity. It is essential to have an optimal number of hidden layers and their corresponding, but attaining those optimal configurations through a manual or trial and the process takes time and makes the process more complex. This urges incorporating optimization approaches for finding optimal hidden layers and their corresponding neurons. The technique involve in configuring the ANN is Mutated Monarch Butterfly Optimization (MMBO). The proposed MMBO employed for configuring the ANN achieves the sensitivity of 97.5% in a real- time database that is superior to contest techniques.

Speaker Recognition Based on Mutated Monarch Butterfly Optimization Configured Artificial Neural Network

Investigating and improving the utility of probabilistic linear discriminant analysis for acoustic signal classification

A bilevel framework for joint optimization of session compensation and classification for speaker identification

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A bilevel framework for joint optimization of session compensation and classification for speaker identification