Hyeon Park¹, SeoYeon Kim², Seok Min Ko¹, TaeGuen Kim^2,*

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. Our experimental results revealed that… More >

R. Gayathri^1,*, 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 extracted by converting the speech… More >

Sneha Basak¹, Himanshi Agrawal¹, Shreya Jena¹, Shilpa Gite^2,*, Mrinal Bachute², Biswajeet Pradhan^3,4,5,*, 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 modern-day developments and applications in… More >

Uğur Ayvaz¹, Hüseyin Gürüler², Faheem Khan³, Naveed Ahmed⁴, Taegkeun Whangbo^3,*, 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 filterbank step in the MFCC… More >

D. Prabakaran^1,*, 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, and many more cloud server… More >

S. Lalitha¹, Deepa Gupta^2,*, 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 similar to that achieved… More >

T. Jayasree^1,*, 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 techniques. In this work, three… More >

Xia Wang¹, Xing Deng^2,3, Hongming Shen^1,*, 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 capture both the local and… More >

Yongqiang Bao^{1, *}, 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) in various environments. Second, the audio… More >

Erdem Yavuz^1,∗, Vedat Topuz²

Computer Systems Science and Engineering, Vol.33, No.6, pp. 429-445, 2018, DOI:10.32604/csse.2018.33.429

Abstract Since Turkish is a morphologically productive language, it is almost impossible for a word-based recognition system to be realized to completely model Turkish language. Due to the fact that it is difficult for the system to recognize words not introduced to it in a word-based recognition system, recognition success rate drops considerably caused by out-of-vocabulary words. In this study, a speaker-dependent, phoneme-based word recognition system has been designed and implemented for Turkish Language to overcome the problem. An algorithm for finding phoneme-boundaries has been devised in order to segment the word into its phonemes. After the segmentation of words into… More >