Punjabi speech to text system for connected words

Dua, Mohit; Aggarwal, Rajesh; Kadyan, Virender; Dua, Shelza

doi:10.1049/cp.2012.2528

Cited by 20 publications

(2 citation statements)

References 2 publications

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“…In the year 2012, another approach was developed by Dua et al, for the recognition of Punjabi automatic speech using the HTK method and HMM modelling system [19]. This study also developed the GUI based system for voice data preparation, acoustic generation and analysis and GUI decoders.…”

Section: Discussion Of Related Workmentioning

confidence: 99%

Voice Biometric Systems for User Identification and Authentication – A Literature Review

K.¹,

Aithal

2022

IJAEML

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Purpose: Biometric trends are used in many systems because of security aspects. The cryptosystem is such an example which uses a biometric. But due to stored biometric data for the authentication, this can be a dangerous issue. Therefore, in comparison to conventional used biometric system, voice biometric system provides an efficient safety, security and unique identity. Among various speech recognition or processing methods, there is one called automated speech conversion methods, which also used to convert the recorded voice into text format. The overall concept of voice reorganization and voice biometric system is based on the acoustic modelling. Therefore, for getting the perfect speech detection, robust acoustic modelling is required. Our analysis describes the advancement and usage of voice biometric system for user identification and authentication. This paper provides a descriptive review of different voice biometric systems, their advancement and applications in different fields. Methodology: The core principles of the research issue have been well discussed in the literature review on speech biometrics. During this process, selected journals from a variety of secondary data sources, such as research papers published in a variety of reputed journals periodicals that are related to the topic are studied in the methodology. Findings/Result: A vocal biometric system is a biological system that captures an individual's voice and assigns it a unique characteristic for authentication purposes. This speech biometric method is primarily used to provide secure, quick, and frictionless access to various electronic devices. In the last three years, rapid technological advancements in neural networks have improved the deployment of speech biometric systems in a variety of industries. The majority of speech biometric system designs are based on the CPU, necessary power, and memory concepts. The advancement of software and hardware interface has been dramatically enhanced and implemented for many applications in the last few years, including smart watches, mobile phones, and car locking systems, where the interface between humans and electronics devices is critical. Banking security, attendance system, file access system, security control, and forensic development system are some of the other commercial applications. Originality: Following the literature study, the findings were utilized to conclude that, despite advances in biometric technology, there is still a significant gap in practical application, particularly for voice biometric systems. When building and developing a voice biometric system, it is necessary to integrate it with an IoT system. Paper Type: Literature Review.

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Section: Discussion Of Related Workmentioning

confidence: 99%

Voice Biometric Systems for User Identification and Authentication – A Literature Review

K.¹,

Aithal

2022

IJAEML

View full text Add to dashboard Cite

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“…Various machine learning techniques Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) [22][23][24], Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), etc. are playing crucial role for classification tasks even in speech based systems [25,26].…”

Section: Introductionmentioning

confidence: 99%

Static–dynamic features and hybrid deep learning models based spoof detection system for ASV

Mittal

Dua

2021

Complex Intell. Syst.

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Detection of spoof is essential for improving the performance of current scenario of Automatic Speaker Verification (ASV) systems. Empowerment to both frontend and backend parts can build the robust ASV systems. First, this paper discuses performance comparison of static and static–dynamic Constant Q Cepstral Coefficients (CQCC) frontend features by using Long Short Term Memory (LSTM) with Time Distributed Wrappers model at the backend. Second, it performs comparative analysis of ASV systems built using three deep learning models LSTM with Time Distributed Wrappers, LSTM and Convolutional Neural Network at backend and using static–dynamic CQCC features at frontend. Third, it discusses implementation of two spoof detection systems for ASV by using same static–dynamic CQCC features at frontend and different combination of deep learning models at backend. Out of these two, the first one is a voting protocol based two-level spoof detection system that uses CNN, LSTM model at first level and LSTM with Time Distributed Wrappers model at second level. The second one is a two-level spoof detection system with user identification and verification protocol, which uses LSTM model for user identification at first level and LSTM with Time Distributed Wrappers for verification at the second level. For implementing the proposed work, a variation in ASVspoof 2019 dataset has been used to introduce all types of spoofing attacks such as Speech Synthesis (SS), Voice Conversion (VC) and replay in single set of dataset. The results show that, at frontend, static–dynamic CQCC feature outperform static CQCC features and at the backend, hybrid combination of deep learning models increases accuracy of spoof detection systems.

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Classical and Deep Learning Data Processing Techniques for Speech and Speaker Recognitions

Mittal

Dua

2021

Signals and Communication Technology

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Punjabi speech to text system for connected words

Cited by 20 publications

References 2 publications

Voice Biometric Systems for User Identification and Authentication – A Literature Review

Voice Biometric Systems for User Identification and Authentication – A Literature Review

Static–dynamic features and hybrid deep learning models based spoof detection system for ASV

Classical and Deep Learning Data Processing Techniques for Speech and Speaker Recognitions

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