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  • Open Access


    Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

    Abhishek Singhal*, Devendra Kumar Sharma

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 609-625, 2024, DOI:10.32604/csse.2023.046730

    Abstract This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions, employing a deep learning classification algorithm for speech signal analysis. In this study, speech samples are categorized for both training and testing purposes based on their geographical origin. Category 1 comprises speech samples from speakers outside of India, whereas Category 2 comprises live-recorded speech samples from Indian speakers. Testing speech samples are likewise classified into four distinct sets, taking into consideration both geographical origin and the language spoken by the speakers. Significantly, the results indicate a noticeable difference… More >

  • Open Access


    CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

    Hyeon Park1, SeoYeon Kim2, Seok Min Ko1, TaeGuen Kim2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1891-1909, 2023, DOI:10.32604/cmc.2023.039464

    Abstract The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. More >

  • Open Access


    Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification

    R. Gayathri1,*, K. Sheela Sobana Rani2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 43-56, 2023, DOI:10.32604/csse.2023.032491

    Abstract Classification of speech signals is a vital part of speech signal processing systems. With the advent of speech coding and synthesis, the classification of the speech signal is made accurate and faster. Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification. In this paper, we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations. Prior classification of speech signals, the study extracts the essential features from the speech signal using Cepstral Analysis. The features are… More >

  • Open Access


    Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

    Sneha Basak1, Himanshi Agrawal1, Shreya Jena1, Shilpa Gite2,*, Mrinal Bachute2, Biswajeet Pradhan3,4,5,*, Mazen Assiri4

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1053-1089, 2023, DOI:10.32604/cmes.2022.021755

    Abstract Speech recognition systems have become a unique human-computer interaction (HCI) family. Speech is one of the most naturally developed human abilities; speech signal processing opens up a transparent and hand-free computation experience. This paper aims to present a retrospective yet modern approach to the world of speech recognition systems. The development journey of ASR (Automatic Speech Recognition) has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper. A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented, along with a brief discussion of various More >

  • Open Access


    Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

    Uğur Ayvaz1, Hüseyin Gürüler2, Faheem Khan3, Naveed Ahmed4, Taegkeun Whangbo3,*, Abdusalomov Akmalbek Bobomirzaevich3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5511-5521, 2022, DOI:10.32604/cmc.2022.023278

    Abstract Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals. One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs are successful in processing the voice signal with high accuracies. MFCCs represents a sequence of voice signal-specific features. This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings. Since the human perception of sound is not linear, after the More >

  • Open Access


    Multi-Factor Authentication for Secured Financial Transactions in Cloud Environment

    D. Prabakaran1,*, Shyamala Ramachandran2

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1781-1798, 2022, DOI:10.32604/cmc.2022.019591

    Abstract The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network (VPN) backbone. This prominent application of VPN evades the hurdles involved in physical money exchange. The VPN acts as a gateway for the authorized user in accessing the banking server to provide mutual authentication between the user and the server. The security in the cloud authentication server remains vulnerable to the results of threat in JP Morgan Data breach in 2014, Capital One Data Breach in 2019,… More >

  • Open Access


    Mental Illness Disorder Diagnosis Using Emotion Variation Detection from Continuous English Speech

    S. Lalitha1, Deepa Gupta2,*, Mohammed Zakariah3, Yousef Ajami Alotaibi3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3217-3238, 2021, DOI:10.32604/cmc.2021.018406

    Abstract Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion. Limited work with standalone speech emotion recognition (SER) systems proposed for continuous speech only has been reported. In the recent decade, various effective SER systems have been proposed for discrete speech, i.e., short speech phrases. It would be more helpful if these systems could also recognize emotions from continuous speech. However, if these systems are applied directly to test emotions from continuous speech, emotion recognition performance would not be… More >

  • Open Access


    Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification

    T. Jayasree1,*, S.Emerald Shia2

    Sound & Vibration, Vol.55, No.2, pp. 141-161, 2021, DOI:10.32604/sv.2021.011734

    Abstract This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks (FFNN). The important pathological voices such as Autism Spectrum Disorder (ASD) and Down Syndrome (DS) are considered for analysis. These pathological voices are known to manifest in different ways in the speech of children and adults. Therefore, it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects. The important attributes hidden in the pathological voices are extracted by applying different signal processing More >

  • Open Access


    Speech Intelligibility Enhancement Algorithm Based on Multi-Resolution Power-Normalized Cepstral Coefficients (MRPNCC) for Digital Hearing Aids

    Xia Wang1, Xing Deng2,3, Hongming Shen1,*, Guodong Zhang1, Shibing Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 693-710, 2021, DOI:10.32604/cmes.2021.013186

    Abstract Speech intelligibility enhancement in noisy environments is still one of the major challenges for hearing impaired in everyday life. Recently, Machine-learning based approaches to speech enhancement have shown great promise for improving speech intelligibility. Two key issues of these approaches are acoustic features extracted from noisy signals and classifiers used for supervised learning. In this paper, features are focused. Multi-resolution power-normalized cepstral coefficients (MRPNCC) are proposed as a new feature to enhance the speech intelligibility for hearing impaired. The new feature is constructed by combining four cepstrum at different time–frequency (T–F) resolutions in order to… More >

  • Open Access


    A Novel System for Recognizing Recording Devices from Recorded Speech Signals

    Yongqiang Bao1, *, Qi Shao1, Xuxu Zhang1, Jiahui Jiang1, Yue Xie1, Tingting Liu1, Weiye Xu2

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2557-2570, 2020, DOI:10.32604/cmc.2020.011241

    Abstract The field of digital audio forensics aims to detect threats and fraud in audio signals. Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech, recognize speakers, and recognize recording devices. User-generated audio recordings from mobile phones are very helpful in a number of forensic applications. This article proposed a novel method for recognizing recording devices based on recorded audio signals. First, a database of the features of various recording devices was constructed using 32 recording devices (20 mobile phones of different brands and 12 kinds of recording pens) More >

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