Effectiveness of different Spectral Features in Replay Attack Detection - An Experimental Study 15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
Assamese Dialect Identification Using Static and Dynamic Features from Vowel Hem Chandra Das, Utpal Bhattacharjee Journal of Advances in Information Technology, 2024 —This paper introduces a novel method for identifying Assamese dialects by analyzing the acoustic and prosodic aspects of vowel sounds in speech signals. The distinctive characteristics of these dialects are captured through the use of acoustic parameters such as formants (F1, F2, and F3), as well as prosodic features like energy, fundamental frequency (F0), and duration. To evaluate this approach, a comprehensive vowel speech corpus is collected from native Assamese speakers representing four different dialectal regions. Frame-level statistical features are extracted from vowel sounds, while temporal dynamic features are obtained from steady-state vowel segments. The data collection process involves using a phonetically rich script to record both read and spontaneous speech interactions from speakers of the four dialects. Various classification methods, including three decision tree-based classifiers, i.e., Random Forest (RF), Extreme Random Forest (ERF), and Extreme Gradient Boosting (XGB), are applied to distinguish the four dialects. The performance of each feature, whether static or dynamic, is individually evaluated. The study reveals that the identification of Assamese dialects is influenced by factors such as speech length, intensity, pitch, and formant frequencies. To assess the significance of these features in distinguishing dialects and to measure their combined impact on the identification system, single-factor Analysis of Variance (ANOVA) tests are conducted. Notably, when static features are combined with the Extreme Random Forest (ERF) ensemble model, the overall accuracy of dialect identification reaches 77%. This research demonstrates the efficacy of using acoustic and prosodic features to accurately classify Assamese dialects, shedding light on the subtle variations within them. In summary, this paper provides a robust framework for Assamese dialect identification and
Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks Debashis Dev Misra, Kandarpa Kumar Sarma, Pradyut Kumar Goswami, Subhrajyoti Bordoloi, Utpal Bhattacharjee International Journal of Mobile Computing and Multimedia Communications, 2024 Routing mechanisms in Ultra-Dense Network (UDNs) are expected to be flexible, scalable, and robust in nature and the establishment of the shortest path between the source and destination pairs will always be a critical challenge. Through this projected work, the optimized shortest route of different source-destination pairs is found using a class of evolutionary optimization algorithms namely PSO, GA, and our proposed hybrid PSO–Genetic Mutation (PSO-GM) algorithm which searches for an optimized solution by representing it as a Shortest Path Routing (SPR) problem. The key attribute of the PSO-GM approach is related to the application of an amalgamated strategy with Gaussian, Cauchy, Levy, Single-point, and Chaos mutation operators. Simulation results and application of the above-mentioned algorithms to the SPR problem in UDNs reveal that the hybrid PSO-GM algorithm provides a comparatively enhanced optimized solution. In the case of the rate of convergence to the theoretical limit, the hybrid PSO-GM gives us 20% better results compared to the PSO and GA.
Assamese Dialect Identification using Semi-supervised Learning Hem Chandra Das, Utpal Bhattacharjee Proceedings 2022 IEEE World Conference on Applied Intelligence and Computing Aic 2022, 2022 The novel approach of this paper is the introduction of a novel information blending paradigm. Two basic classifiers are first trained using the shifting delta cepstra (SDC) and prosodic features, respectively. To improve Assamese dialect detection accuracy, the co-training approach is used in semi-supervised learning. This system evaluates four Assamese dialects. The experimental data show that the suggested system performs better than the existing system that used GMM.
Review of performance factors of emotional speaker recognition system: Features, feature extraction approaches and databases Satish Kumar Das, Uttpal Bhattacharjee, Amit Kumar Mandal Reliability Theory and Applications, 2021 Emotion is a conscious mental reaction accompanied by physiological and behavior changes in human body.In speaker authentication system, emotional state of the speaker plays a vital role. Recently, the field of speaker recognition in emotional context attracts more and more attention of many research focuses. However, to implement more realistic and intelligent emotional speaker recognition system it is interesting to study this system under real life conditions. Speech emotion recognition is a system in which speech signals are processed to classify the embedded emotions. In recent past, speaker emotion recognition has gained a lot of attention from different researchers as it has many applications. In this regards, study of prior works is useful for further research in the field of speaker verification in emotional context. So, performance and reliability of Emotional Speaker Recognition System depend on the proper selection of features to characterize different emotional states, feature extraction approaches and databases. In this paper we briefly discuss about different features, feature extraction approaches and emotion recognition and speaker verification databases.
Optimal Routing in the 5G Ultra Dense Small Cell Network using GA, PSO and Hybrid PSO-GA Evolutionary Algorithms Debashis Dev Misra, Kandarpa Kumar Sarma, Utpal Bhattacharjee, Pradyut Kumar Goswami, Nikos Mastorakis Proceedings 24th International Conference on Circuits Systems Communications and Computers Cscc 2020, 2020 Ultra Dense Network (UDN) is a guiding principle in the direction of 5G network challenges which focuses on network infrastructure densification. The extremely dynamic nature of such networks implies that the optimal path between the source/destination pairs will be highly dynamic in nature. In such a backdrop, routing mechanisms have to be designed to be robust and scalable in nature in order to ensure seamless link reliability and quality of service of the network. The shortest optimal route of the source/destination pair is found using a combination of evolutionary optimization algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm and our proposed hybrid PSO-GA which searches for an optimal option by determining cost functions of individual fitness state and comparing states generated between individual solutions. Applying all the three proposed algorithms to the Shortest Path routing problem in UDNs has led us to believe that the hybrid PSO-GA has given a comparatively better solution.
Performance Evaluation of Normalization Techniques in Adverse Conditions Renu Singh, Utpal Bhattacharjee, Arvind Kumar Singh Procedia Computer Science, 2020 This paper explores the behavior of different normalization techniques viz. cepstral mean normalization, cepstral variance normalization, cepstral mean subtraction. cepstral mean and variance normalization, wiener filter, and spectral subtraction in noisy conditions. The performance parameters viz. EER (Equal Error Rate) and DCF (Detection Cost Function) has been calculated using NIST 2003 SRE and Aurora 2 with the help of various normalization techniques considered in this paper for different noisy backgrounds at 0, 5 and 10 dB signal-to-noise ratio. The experimental results obtained from these techniques reveal that cepstral mean normalization (CVN) normalization method is found to be better when compared to other normalization techniques used in this paper.
Enhancement of Multilingual Speaker Verification System with a New Approach 11th Indiacom 4th International Conference on Computing for Sustainable Global Development Indiacom 2017, 2017
