A real time speech to text conversion system using bidirectional Kalman filter in Matlab

Sharma, Neha; Sardana, Shipra

doi:10.1109/icacci.2016.7732406

Cited by 15 publications

(3 citation statements)

References 15 publications

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“…The proposed method developed a voice biometric system which was used to differentiate various languages. In year 2016, Neha et al, demonstrated the Designing a Real-Time Speech Recognition System using MATLAB [28]. In this study specific, nine words were collected and analysed which were separated with respect to associated energies.…”

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

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“…This paper uses MVDR and DNN algorithms for building automatic speech recognition technique. Neha S. et al [30] implemented a bi-directional speech to text conversion system. This paper introduces a technique for speech recognition which uses Bidirectional nonstationary KALMAN FILTER algorithm for building ASR.…”

Section: Types Of Asr Systemsmentioning

confidence: 99%

Automatic Speech Recognition Systems for Regional Languages in India

2019

ijrte

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Speech recognition systems has made remarkable progress in last ¬few decades such as Siri, Google assistant, Cortana. For improving the automation in services of all sectors including medical, agriculture, voice dialling, directory services, education, automobile etc., ASR systems must be built for regional languages as most of the Indian population in not familiar with English. Lots of work is done for English language but not for regional languages in India. Developing ASR and ASU systems will change the scenario of current service sector. There are many challenges in building ASR system, Noise reduction is a one of the challenging and still unsolved parameters which affects a lot on performance of any ASR system. Basically, three models required for building any ASR systems- Language model, acoustic model and pronunciation model. In this paper, discussed various parameters affecting on building ASR systems, development of ASR systems, Tools and Techniques used for building an ASR system and research on regional languages ASR system. Deep Neural network (DNN) provides a better way of recognising a speech and accuracy is high.

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“…Kalman filter can be used in speech recognition to remove the unwanted noise and to get filtered output. Kalman filtering is a state estimator that produces an optimal estimate and minimizes the mean square error [46]. The utterance-level Permutation Invariant Training (uPIT) is a practical technique for speaker independent multi-talker speech separation [47].…”

Section: Speech To Text Conversionmentioning

confidence: 99%

Call Transcription and Text Classification - A Review

Arivazhagan¹,

Anaswara²,

Eliza³

et al. 2018

IJCA

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A common method of data exchange is speech. Keeping track of every word exchanged through verbal means is difficult. Mostly, verbal communication is carried out through voice calls. For keeping tab of information exchanged, calls can be converted to text. These text files can be classified according to the content of the document. Also, unwanted call recordings have to be eliminated. This research contains the various methods required to implement call transcription and text classification. More than 50 papers were reviewed regarding the sections, call recording, transcription and ordering of text documents.

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A real time speech to text conversion system using bidirectional Kalman filter in Matlab

Cited by 15 publications

References 15 publications

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

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

Automatic Speech Recognition Systems for Regional Languages in India

Call Transcription and Text Classification - A Review

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