Artificial Intelligence and Speech Technology - Taylor & Francis

Artificial Intelligence and Speech Technology

The 2nd International Conference on Artificial Intelligence and Speech Technology (AIST2020) was organized by Indira Gandhi Delhi Technical University for Women, Delhi, India on November 1920, 2020. AIST2020 is dedicated to cutting-edge research that addresses the scientific needs of academic researchers and industrial professionals to explore new horizons of knowledge related to Artificial Intelligence and Speech Technologies. AIST2020 includes high-quality paper presentation sessions revealing the latest research findings, and engaging participant discussions. The main focus is on novel contributions which would open new opportunities for providing better and low-cost solutions for the betterment of society. These include the use of new AI-based approaches like Deep Learning, CNN, RNN, GAN, and others in various speech related issues like speech synthesis, speech recognition, etc.

Editors

Amita Dev, Vice-Chancellor, Indira Gandhi Delhi Technical University for Women, Delhi

Arun Sharma, Professor – IT, Indira Gandhi Delhi Technical University for Women, Delhi

S. S. Agrawal, Emeritus Scientist, CSIR

Artificial Intelligence and Speech TechnologyProceedings of 2nd International Conference on Artificial Intelligence and Speech Technology, (AIST2020), November 19–20, 2020, Delhi, India

Edited by

Amita Dev, Arun Sharma, and S. S. Agrawal

First edition published 2021 by CRC Press2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN

and by CRC Press6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742

© 2021 selection and editorial matter, Amita Dev, Arun Sharma, S. S. Agrawal; individual chapters, the contributors

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Contents

Organisation xiPreface xiii

1 Classification approaches for automatic speech recognition system 1 Amritpreet Kaur, Rohit Sachdeva, Amitoj Singh

2 Early detection of PCOD using machine learning techniques 9 Pratik Wagh, Megha Panjwani, and Amrutha S.

3 Application of real-time object detection techniques for bird detection 21 Neha Sharma, Reetvik Chatterjee, Akhil Bisht, and Harit Yadav

4 Machine learning algorithms used for detection of prostate cancer 27 Rajesh M. N. and Chandrasekar B. S.

5 How training of sigmoidal FFANN affected by weight initialization 37 Veenu

6 Machine learning for web development: A fusion 45 Parita Jain, Anupam, Puneet Kumar Aggarwal, Kshirja Makar, Vineet Shrivastava,

and Seema Maitrey

7 Bot attack detection using various machine learning algorithms 53 Sanjay Madan, Shivani Arya, Divya Bansal, and Sanjeev Sofat

8 Present scenario of emotionally intelligent voice-based Conversational Agents in India 63

Surbhi Khurana and Amita Dev

9 Blockchain-based secured data transmission of IoT sensors using thingspeak 77

Monika Parmar, Harsimran Jit Kaur, and Rinku

vi

CONTENTS

10 Impact of energy storage device on the performance of AGC using ALO tuned PID controller 87

Choudhary R., Rai J. N., and Arya Y.

11 The instrument to measure happiness at workplace 97 Shweta Singh, Nupur Pandey, and Arun Kumar Tripathi

12 IoT based smart cyber sealing system 105 Tanishqa Chaudhary, Kshitija Patil, Abhishek Katore, Mohandas Pawar, and Sagar Jaikar

13 A novel approach for summarizing legal judgements using graph 113 Vanyaa Gupta, Neha Bansal, Arun Sharma, and Kaustubh Mani

14 Deep CNN architectures for learning image classification: A systematic review, taxonomy and open challenges 121

Nagaraju M. and Priyanka Chawla

15 The quest for crop improvement in the era of artificial intelligence, machine learning, and other cognitive sciences 129

Pushpikka Udawat, Jogendra Singh, and Ameta G. S.

16 A run-through: Text independent speaker identification using deep learning 139

Pooja Gambhir and Amita Dev

17 Summarization of video lectures 149 Ashwin Siddharth S., Yogesh B. R., Rutvik B., and Jayashree R.

18 Artificial intelligence approach in video summarization 159 Mudit Saxena and Gangadharappa M.

19 Extractive summarization of recorded Odia spoken feedback 171 Basanta Kumar Swain, Sanghamitra Mohanty, and Dillip Ranjan Nayak

20 Frame change detection in videos – challenges and research directions 179

Alina Banerjee, Ravinder M., and Ela Kumar

21 Speech impairment recognition using XGBoost classifier 187 Renuka Arora and Sunny Arora

22 Research insight of Indian tonal languages: A review 195 Jaspreet Kaur Sandhu and Amitoj Singh

23 Advances in speech vocoding for text-to-speech with continuous parameters 203

Mohammed Salah Al-Radhi, Tamás Gábor Csapó, and Géza Németh

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CONTENTS

24 Applying entity recognition and verb role labelling for information extraction of Tamil biomedicine 211

Betina Antony J., Rejin Paul N. R., and Mahalakshmi G. S.

25 Identification of two tribal languages of India: An experimental study 221 Joyanta Basu, Theodore Raphael Hrangkhawl, Tapan Kumar Basu,

and Swanirbhar Majumder

26 Mental illness diagnosis from social network data using effective machine learning technique 231

Shivani Singh and Sandhya Tarar

27 Hybrid classifier for brain tumor detection and classification 239 Ritu Garg and Om Prakash Sangwan

28 Parametric study of through transmission laser welding with teaching learning based optimization 247

Rituparna Ghosh

29 Research landscape of artificial intelligence in human resource management: A bibliometric overview 255

Mandeep Kaur, Rekha A. G., and Resmi A. G.

30 An efficient Class-F PA with SSL/SIL based matching network for body centric wireless transceiver 263

Puja Priya, Gurjit kaur, and Rajesh Kumar

31 Envisaging the future homes with ‘human-building interaction’ 271 Harsha Deivalakshmi Thirunavukkarasu, Vriti Sachdeva, and Kshitij Kumar Sinha Ar.

32 Comparative analysis of first-order optimization algorithms 279 Nitin Yadav, Satinder Bal Gupta, and Raj Kumar Yadav

33 A review on application of artificial intelligence techniques in control of industrial processes 291

Pradeep Kumar Juneja, Neha Belwal, Sandeep Kumar Sunori, Farha Khan, Abhinav Sharma, and Gaurav Pundir

34 Crop monitoring system for effective prediction of agricultural analytics in Indian agriculture using WSN 297

B. Balaji Bhanu, Mohammed Ali Hussain, and Mahmood Ali Mirza

35 Trend analysis of meteorological index SPI using statistical and machine learning models over the region of Marathwada 315

Rashmi Kumaria Nitwane, Bhagile V. D., and Deshmukh R. R.

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CONTENTS

36 An insight into reconfigurable antenna design 329 Pramod Singh and Rekha Aggarwal

37 Optimized XGBoost algorithm using agglomerative clustering for effective user context identification 339

Sunita Kumari Chauraisa, and Reddy S. R. N.

38 1D CNN based approach for speech emotion recognition using MFCC features 347

Youddha Beer Singh and Shivani Goel

39 Review on text detection and recognition in images 355 Mahima Kataria, Prashansa Gupta, Shivani Singh, Vani Bansal, and Ravinder M.

40 Comparative analysis of machine learning algorithms on gender classification using Hindi speech data 363

Kanika Gupta, Arun Sharma, and Mohapatra A. K.

41 COVID-19 detection through Mamdani-based fuzzy inference system 371

Usha Mittal, Sayantan Kar, and Priyanka Chawla

42 Assistive technology is a boon or bane: A case of persons with disabilities 379

Shalini Garg, Aarti Sehgal, and Snehlata Sangwan

43 Sensor data fusion using machine learning techniques in indoor occupancy detection 387

Pushpanjali Kumari, Reddy S. R. N., and Richa Yadav

44 Covid’19 virus life progress span by using machine learning algorithms and time series methods 399

Neelam Rawat, Prashant Agrawal, Arun Kumar Tripathi, and Yashpal Singh

45 Sustainable development through adoption of digitization towards functioning of self help groups 411

Archana Singh and Sarika sharma

46 Security and vulnerability issues in NoSQL 421 Laxmi Ahuja, Ajay Rana, and Prina Todi

47 Artificial intelligence applications and techniques in interactive and adaptive smart learning environments 427

Divanshi Priyadarshni Wangoo and Reddy S. R. N.

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CONTENTS

48 SIM-BERT: Speech intelligence model using NLP-BERT with improved accuracy 439

Pankaj Kumar and Sanjib Kumar Sahu

49 A literature review on virtual assistant for visually impaired 447 Lavisha Malik, Lavanya Gaur, Disha Goyal, Manisha Yadav, and Ravinder M.

50 Congestion control mechanisms to avoid congestion in VANET: A comparative review 453

Rajni Sharma, Lamba C. S., and Rathore V. S.

51 Comparative Study of various Stable and Unstable sorting Algorithms 463

Ravi Yadav, Rajkumar Yadav, and Satinder Bal Gupta

52 Prediction analysis of forecasting applications with concept drifting distributions 479

Kanu Goel and Shalini Batra

53 A hybrid cardiovascular disease prediction system using machine learning algorithms 487

Muneer, V. K., Ramseena, N., Rizwana Kallooravi Thandil, and Mohamed Basheer, K. P.

54 Financial inclusion via Fintech: A conceptual framework for digitalizing the banking landscape of rural India 497

Shreya Virani, Sonica Rautela, and Sarika Sharma

xi

Organisation

General Chair: Dr. Amita Dev, Vice-Chancellor-IGDTUW

Technical Program Chair: Dr. S S Agrawal, Former Emeritus Scientist-CSIR

Conference Secretary: Dr. Arun Sharma, Prof., and Head, Dept. of IT - IGDTUW

Technical Program Committee

Dr. S. Nakamura, Nara Institute of Science and Technology, JapanDr. Nemeth Geza, Budapest University of Technology and Economics, HungaryDr. Milan Stehlik, Johannes Keplar University, AustriaDr. Barbara Zitova, Czech RepublicDr. Chai Wutiwiwatchai, NECTEC, ThailandDr. Hsin-Min Wang, IIS, TaiwanDr. Steven Bird, CDU, AustraliaDr. Ashish Seth, Inha Univ., TashkentDr. Ayu Purwarianti, ITB, IndonesiaDr. Danill Kocharov, HKUST, ChinaDr. Dirk Van Compernolle, KU, BelgiumDr. E. Dupoux, EHESS-ENS, FranceProf. Anand Nayyar, Duy Tan University, Da Nang, VietnamDr. Eric Castelli, MICA, VietnamDr. Etienne Barnard, NWU, South AfricaDr. Gerard Bailly, CNRS, France

Dr. Gilles Adda, CNRS, FranceDr. Brian Mak, HKUST, ChinaDr. Charl Van Heerden, SPbSU, RussiaDr. Claudia Soria, ILC-CNR, ItalyDr. Tan Tien Ping, USM, MalaysiaDr. Tanja Schultz, Uni-Bremen, GermanyDr. Thang Vu, Uni-Stuttgart, GermanyDr. Win Pa Pa, UCS Yangon MyanmarDr. Xavier Anguera, Telefonica, SpainDr. Y. Sagisaka, Waseda UniversityDr. Yuri Matveev, ITMO Univesity, RussiaDr. Zuraida Mohd Don, UPSI IndonesiaDr. Joseph Mariani, CNRS, FranceDr. Laurent Besacier, LIG, FranceDr. Lori Lamel, LIMSI, FranceDr. Luong Chi Mai, IOIT, VietnamDr. Mariko Kondo, Waseda Univ., JapanDr. Marelie Davel, (NWU), South AfricaDr. Mirna Adriani, Univ. of Indonesia,

xiixii

ORGANISATION

Dr. Pascal Nocera, LIA, FranceDr. Pavel Skrelin, SPbSU, RussiaDr. Pedro Moreno, Google, USADr. Alexey Karpov, SPIIRAS, RussiaDr. Sakriani Sakti, NAIST, JapanDr. Sebastian Stüker, KIT, GermanyDr. Sin Horng Chen, NCTU, TaiwanDr. Nick Campbell, Trinity College Dublin, Dr. P.C. Ching, Chinese Univ. of H.K.,Dr. Hiroya Fujisaki, Tokyo UniversityMs. Mona Bharadwaj, IBM (I) Pvt. Ltd.Dr. Mani Madhukar, IBM (I) ndia Pvt. Ltd, Mr. Khundmir Syed, IBM (I) Pvt. Ltd.Mr. Kranti Athalye, IBM (I) Pvt. Ltd.Ms. Shikha Maheshwari, IBM (I) Pvt. Ltd.Ms. Ruchika Gupta, IBM (I) Pvt. Ltd.Ms. Anu Khosla, SAG DRDO, India

Dr. S. K. Shrivastava, DRDO, IndiaDr. S. K. Jain, CFSL, IndiaDr. Vipin Tyagi, JUIT, Guna, IndiaDr. Arun Kumar, IIIT-Delhi, IndiaDr. P. K. Saxena, PSA, PMO, IndiaDr. Anupam Shukla, IIIT-Pune, IndiaDr. Karunesh Arora, CDAC, Noida, IndiaDr. Somnath Chandra, DEITY, Delhi,Dr. Sudip Sanyal, BML Munjal University, Dr. Poonam Bansal, MSIT, Delhi, IndiaDr. Swaran Lata, DEITY, IndiaDr. Sunita Arora, CDAC, Noida, IndiaDr. Kamini Malhotra, SAG, DRDO, IndiaDr. Samudra Vijaya K, IIT-Guwahati, Dr. P. K. Das, IIT Guwahati, IndiaDr. Omar Farooq, AMU, IndiaDr. Shweta Sinha, KIIT, Gurgaon, India

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Preface

It gives us immense pleasure to present the Proceedings of the Second International Conference on “Artificial Intelligence and Speech Technology” AIST-2020 organized by Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India on November 19–20, 2020. We hope that you will find it useful, exciting, and inspiring.

The first version of the Conference was a great success with the participation of experts from Japan, Hungary, Czech Republic, Myanmar, and almost all corners of India. The 2nd version of AIST has scaled up with more quality papers from a wider reach of academicians, professionals, and researchers all over the world. The technology giant IBM has agreed to provide technical support for the Conference. Due to the pandemic situation all around the world, the Conference is organized in an online mode which has provided us the opportunity to interact with more International Keynote Speakers and to learn from these experts.

The conference aims to serve as a forum for discussions on the state-of-the-art research, development, and implementations of Artificial Intelligence and Speech Technology. AIST-2020 is dedicated to cutting-edge research that addresses the scientific needs of academic researchers and industrial professionals to explore new horizons of knowledge related to Artificial Intelligence and Speech Technology. Researchers from across the world are presenting their research revealing the latest and relevant research findings on almost all the aspects of Artificial Intelligence and Speech Technology.

As academicians, the responsibility to nurture complete professionals lies with us. This necessitates the knowledge of the latest trends in fast-changing technology. Conferences bring together people from all different geographical areas who share a common discipline or field and are found effective to extend one’s knowledge.

We would like to express our sincere thanks and appreciation to the world-renowned Professors and prominent Researchers for having agreed to deliver the keynote session and share their knowledge during the Conference. We are thankful to Taylor and Francis for agreeing to publish the Conference Proceedings as an eBook for a wider reach of the research work presented during the Conference to other researchers and Practitioners.

xiv

PREFACE

We are sure that this colloquy of researchers and experts from academia and industry would greatly benefit researchers, students, and faculty. Young scientists and researchers will find the contents of the proceedings helpful to set roadmaps for their future endeavors.

Amita DevArun Sharma

S S Agrawal

Chief Guest (Inauguration Function)

Prof. V. Ramgopal Rao - Director, IIT Delhi

Key Note Speakers

Dr. S. NakamuraNAIST, Japan

Dr. Nemeth GezaBudapest Univ.,

Hungary

Dr. Amita DevVice-Chancellor,

IGDTUW

Dr. Win Pa PaUCSY, Myanmar

Dr. Manish BhideChief Architect,

IBM

Dr. Milan Stehlik,Johannes Kepler

University, Austria

Dr. S. UmeshIndian Institute of

Technology - Madras

Dr. S. S. AgrawalEmeritus

Scientist, CSIR

Session Chairs

Dr. Samudra Vijaya, IIT Guwahati

Dr. Ashish SethInha University,

Tashkent

Dr. Anand Nayyar, Duy Tan Univ.

Viet Nam

Dr. Karunesh Arora, CDAC, Noida

Dr. Swaran Lata Head-TDIL

(Retd) MeitY, GoI

Dr. P. K. SaxenaPr.

Scientific Advisor – Fellow, GoI

Dr. S. K. JainDirector cum Chief Forensic Scientist

MHA, GoI

Dr. Priyanka Chawla, LPU

Dr. Ihtram Raza Khan Jamia

Hamdard, Delhi

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CHAPTER 1

Classification approaches for automatic speech recognition systemAmritpreet Kaur a, Rohit Sachdevab, Amitoj Singhc

a,bM.M. Modi College, Patiala cMRS PTU, [email protected]

Abstract: Recognition of Speech is now becoming more widespread. Different applications that are knowledgeable of interactive expression are present on the market. For those devices in which handwriting is complicated, speaking recognition systems are sensible options. With growing specifications for embedded devices and modern embedded technologies, the Speech Recognition Systems (SRS) must also be available. Mainly the latest expressions use Hidden Markov Models (HMMs) methods to decide how well every condition of each HMM fit in with a picture or effective allocation of coefficient frames that reflect acoustical inputs, to interact with the spatial uncertainty of language and Gaussian Mixture Models (GMMs). Alternatively, the use of a neural feed method that uses many structures of coefficients as inputs and creates later chances as output in relation to HMM states. Deep Neural Networks (DNN) with many input layer which are equipped with modern techniques have already shown that GMMs are more successful on a range of voice recognition criteria, with many input nodes. DNNs have been equipped with new techniques to surpass GMMs on a number of speech recognition criteria, often by a significant margin. This study offers an analysis of development and reflects the common perspectives of four study groups who have recently found excellence in the use of deep neural networks in speech recognition for acoustic modeling.

Keywords: DNN, GMM, HMM, RASTA

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ARTIFICIAL INTELLIGENCE AND SPEECH TECHNOLOGY

1.1 Introduction

The only way in which humans can exchange their information with each other is speech (Allen, 1995). Speech processing is one of the methods to provide this interaction between human and machine. Speech Recognition (or) more popularly known as Automatic Speech Recognition (ASR) has gained lots of popularity since last few years (Abdel-Hamid et al., 2014; Amodei et al., 2016; O’Shaughnessy, 2008). Acknowledgment of expression is a part of deep learning with computers that aid human beings to interact more easily and closely to the machines. Speech Recognition has become an essential part of every application whether it’s your smartphone to a well-established web application. The voice search feature is now gaining great popularity nowadays. Voice recognition has been included by everyone as the important part of their lives. Speech has arisen as one of the simplest modes for man to machine and machine to man interaction thanks to recent research in technology (Cyran, Kozielski, Peters, Stanczyk, & Wakulicz-Deja, 2009).

1.2 Methods

There are different ways to perform the speech recognition functions. Notable research has been done to make a zero-loss information speech system.

• A leading encoding strategy was used in the mathematical context for HMM. This is an inter-related method, generated by two processes, a Markov chain which is rooted in a limited variety of states, and a particular function of probabilities is correlated with each of these states, to determine the odds of the acoustic properties. State probabilities can be modeled through continuous likelihood function, semi-continuous production of probability, or constant production of probability. Mixture distributions consisting of a linear combination of Gaussian or Laplacian density functions are the typical models for the constant probability distribution.

• ANN is a computer device driven by the arrangement of cell types in the living person’s mind. The most commonly used category of ANN in speech recognition (Masmoudi, Frikha, Chtourou & Hamida 2011) is Multi-Layer Perceptron (MLP). A potential substitute to substitute or assist HMM in classification style was digital NN & and more precisely MLP. Some ANN methods to develop state-of-the-art ASR structures were suggested (Fauziya & Nijhawan, 2014; Renjith & Manju, 2017).

• Deep learning methods are proposed as an advancement of ANN in order to get significant improvement in the performance of the acoustic models. Restricted Boltzmann machine, Deep Belief Networks (DBN) (A.-R. Mohamed, Dahl, & Hinton, 2009; A. R. Mohamed, Dahl, & Hinton, 2012), Deep Neural Networks (DNN) (Hinton et al., 2012; Senior, Heigold, Bacchiani, & Liao, 2014), Convolutional Neural Networks (CNN) (Abdel-Hamid et al., 2014; Passricha & Aggarwal, 2019a), Capsule Network are popular variants of deep learning technique that is successfully adopted speech recognition tasks. Evolutionary Techniques search approaches focused on the theory of natural evolution are adaptive methods such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), etc. These strategies have the potential to produce a simple community of potential responses and a really strong capacity to identify the best approaches of all those reasonable solutions (Dua, Aggarwal, & Biswas, 2018; Passricha & Aggarwal, 2019b).

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ClassifiCation approaChes for automatiC speeCh reCognition system

1.3 Architecture of Speech Recognition

The architecture of ASR system which has two main components is broadly divided into 5 components as shown in Figure 1.1.

Feat

ures

Training

Speech Transcription

Testing

Corpus

Speech Signal

Speech Text

Acoustic Model

Language Model

Model Generation

Para

met

ric

DecoderPreprocessing/ Feature Extraction

Figure 1.1 The architecture of automatic speech recognition system

1.3.1 Preprocessing and Feature Extraction

The key aim of extraction is to project the speech signal in a compact parameter space by merely extracting data associated with spoken language. Certain standard techniques of extraction are:

1.3.1.1 LPC: A sufficient number of past samples will estimate the speaking sample. LPC is determined to reduce predictive error. The human pronunciation vessels are represented by signal detection techniques during this procedure.

1.3.1.2 MFCC: The voice signal first is evaluated in the form of STFT and then the DFT principles are integrated into essential groups and weighed by triangular measurement method. This approach is based on the auditory function of humans. PLP: In PLP, the LPC and MFCC characteristics are combined to achieve improved performance. Instead of trapezoidal filters, the triangular philter has been used here (Hermansky, 1990).

Along with above mentioned techniques number of the other techniques like RASTA, PLDA, LPCC etc. are modeled by signal processing techniques (Hermansky & Morgan, 1994; Makhoul, 1975).

1.4 Models of Speech Recognition

There are different types of models that are needed for Automatic Speech Recognition. Figure 1.2 shows the major speech recognition components. The models are mentioned as below:

1.4.1 Acoustic Modeling: To recognize an unknown utterance, the extracted features have to be compared with some reference models. This reference model is called acoustic model. The

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acoustic model processes the feature vectors either directly or by using a phoneme-based model are the two categories of the acoustic model (Hinton et al., 2012).

1.4.2 Pronunciation Modeling: The pronunciation model or lexicon model grants the explanation of lexicons in phrase of basic sub-lexicon components (phoneme) that are present in system vocabulary. It is developed to impart the accent of each word available in the vocabulary. A pronunciation model in an ASR system is used to provide the mapping between the vocabulary and the training model. Different phone combinations are used to represent the word in the lexicon model. The Lexicon model typically uses normal expression in a normal dictionary (Liu & Fung, 2004).

1.4.3 Language Modeling: The importance of the language model for ASR systems can be understood from the fact that many of the state-of-the-art speech recognition applications would have been impossible to implement without an effective language model and constrained grammars (Chen, Liu, Gales, & Woodland, 2015; Jozefowicz, Vinyals, Schuster, Shazeer, & Wu, 2016).

Training Data

Representation Search

Recognized Words

Speech Signal

Applying Constraints

Language Models

Lexical Models

Acoustic Models

Figure 1.2 Given below shows the major components for speech recognition

1.5 Comparative Analysis of SR Models

After going through the vast literature available till yet in the field of speech recognition different techniques were studied. The table given below highlights these techniques along with their advantages and disadvantages.

Table 1.1 Comparative study of speech recognition modeling technique

Sr. No.

SRS Techniques Advantages Disadvantages

1. Confirmation of Acoustic phonology (Siniscalchi & Lee, 2009)

1. Decreases the linked terms processing time

1. Unused for commercial purposes due to the long-running of each single word

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2. Wrapping with Dynamic Time (Müller, 2007)

1. Because of missing details, it can fit process. The continuity is less important.

2. Reliable time alignment

1. It coincides with some constraints between two specified sequences.

2. Intensive programming work takes full time.

3. Pattern Recognition Approach (Fukunaga, 1993)

1. Just because word to word matching occurs it recognize pattern quickly and easily.

1. It is effective for adjusting word to word.

2. The primary concern is the prototype and sluggish operation.

4. Strategy to the Digital Network (Yegnanarayana, 2009)

1. Can be successful in addressing complicated requests in a less time span.

2. The capability to develop data automatically and instructed the device to adapt without mistake from the original model.

1. Offer the broad vocabulary an inefficient outcome

2. It is costly because it needs a lot of preparation on a wide volume.

3. No complete understanding is yet made of the full existence of the neural network.

4. It takes more time to practice.

5. Statistical Based Approach (HiddenMarkov method) (Baggenstoss, 2001; Gales & Young, 2008; Jiang, Li, & Liu, 2006)

1. HMM has a very strong language so that it can develop a lot of info.

2. The mathematical structure work is detailed.

3. The qualified algorithms can be found easily.

4. Can be easily applied, and everyone can simply modify these models’ scale, form and design into a special term.

1. The sophistication of the machine has risen considerably.

2. A lot of data is necessary.

1.6 Discussion and Future Scope

In the field of speech recognition, an immense number of directions can be explored to carry out further research as the proposed techniques being used for extracting the features of the speech samples can be extended further by combining different techniques to improve the speech recognition rate and WER. The researchers should develop standard databases for languages. The LVCSR database systems should be focused more. The databases should be made accessible to the

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researchers so that future research can nurture. Many languages have not been employed efficient feature extraction techniques. Researchers have only focused baseline MFCC features as studied in the prevalent literature. Hybrid features like MFCC+LDA+MLLT, MFCC+BFCC+GFCC, LDA+MFCC, MF+PLP, RASTA-PLP etc. may be implemented. Researchers in the studied literature have reported their results of recognition accuracy in clean i.e. noise-free environments.

1.7 Conclusion

The Speech Recognition System (SRS) has limitless implementations and expands with each day. The research presented a description of the method of language processing, its underlying model and implementations. The author suggests that efficient feature extraction techniques are implemented on the English language. This is because the research area of speech is tremendously wide. Also, not many accurate ASR systems have been developed yet which can be efficient for continuous speech. Furthermore, LVCSR systems should be explored more by the researchers in order to improve accuracy of recognizing speech for such systems so that applications for real-time use can be developed. In this comparative analysis, the hidden Markov model is the best solution to SRS as it is powerful, resilient and decreases time and diverse.

References

Abdel-Hamid, O., Mohamed, A. R., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014). Convolutional Neural Networks for Speech Recognition. IEEE-ACM Transactions on Audio Speech and Language Processing, 22(10), 1533–1545. doi:10.1109/Taslp.2014.2339736

Allen, J. B. (1995). How do humans process and recognize speech? In Modern methods of speech processing (pp. 251–275): Springer.

Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., . . . Chen, G. (2016). Deep speech 2: End-to-end speech recognition in English and Mandarin. Paper presented at the International Conference on Machine Learning.

Baggenstoss, P. M. (2001). A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. IEEE transactions on speech and audio processing, 9(4), 411–416. Chen, X., Liu, X., Gales, M. J., & Woodland, P. C. (2015). Recurrent neural network language model training with noise contrastive estimation for speech recognition. Cyran, K. A., Kozielski, S., Peters, J. F., Stanczyk, U., & Wakulicz-Deja, A. (2009). Man-Machine Interactions (Vol. 59): Springer Science & Business Media.

Dua, M., Aggarwal, R. K., & Biswas, M. (2018). GFCC based discriminatively trained noise robust continuous ASR system for Hindi language. Journal of Ambient Intelligence and Humanized Computing, 1–14. Fauziya, F., & Nijhawan, G. (2014). A Comparative study of phoneme recognition using GMM-HMM and ANN based acoustic modeling. International Journal of Computer Applications, 98(6). Fukunaga, K. (1993). Statistical pattern recognition. In Handbook of pattern recognition and computer vision (pp. 33–60): World Scientific.

Gales, M., & Young, S. (2008). The application of hidden Markov models in speech recognition. Foundations and Trends® in Signal Processing, 1(3), 195–304. Hermansky, H. (1990). Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America, 87(4), 1738–1752. Hermansky, H., & Morgan, N. (1994). RASTA processing of speech. IEEE transactions on speech and audio processing, 2(4), 578–589. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-R., Jaitly, N., . . . Sainath, T. N. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82–97. Jiang, H., Li, X., & Liu, C. (2006).

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Large margin hidden Markov models for speech recognition. IEEE transactions on audio, speech, and language processing, 14(5), 1584–1595. Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., & Wu, Y. (2016). Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410. Liu, Y., & Fung, P. (2004). State-dependent phonetic tied mixtures with pronunciation modeling for spontaneous speech recognition. IEEE transactions on speech and audio processing, 12(4), 351–364. Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561–580. Masmoudi, S., Frikha, M., Chtourou, M., & Hamida, A. B. (2011). Efficient MLP constructive training algorithm using a neuron recruiting approach for isolated word recognition system. International Journal of Speech Technology, 14(1), 1–10. Mohamed, A.-R., Dahl, G., & Hinton, G. (2009). Deep belief networks for phone recognition. Paper presented at the Nips workshop on deep learning for speech recognition and related applications.

Mohamed, A. R., Dahl, G. E., & Hinton, G. (2012). Acoustic Modeling Using Deep Belief Networks. IEEE Transactions on Audio Speech and Language Processing, 20(1), 14–22. doi:10.1109/Tasl.2011.2109382

Müller, M. (2007). Dynamic time warping. Information retrieval for music and motion, 69–84. O’Shaughnessy, D. (2008). Automatic speech recognition: History, methods and challenges. Pattern Recognition, 41(10), 2965–2979. Passricha, V., & Aggarwal, R. K. (2019a). End-to-End Acoustic Modeling Using Convolutional Neural Networks. In Intelligent Speech Signal Processing (pp. 5–37): Elsevier.

Passricha, V., & Aggarwal, R. K. (2019b). PSO-based optimized CNN for Hindi ASR. International Journal of Speech Technology, 22(4), 1123–1133. Renjith, S., & Manju, K. G. (2017, 20–21 April 2017). Speech based emotion recognition in Tamil and Telugu using LPCC and hurst parameters—A comparitive study using KNN and ANN classifiers. Paper presented at the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

Senior, A., Heigold, G., Bacchiani, M., & Liao, H. (2014). GMM-free DNN acoustic model training. Paper presented at the Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on.

Siniscalchi, S. M., & Lee, C.-H. (2009). A study on integrating acoustic-phonetic information into lattice rescoring for automatic speech recognition. Speech communication, 51(11), 1139–1153. Yegnanarayana, B. (2009). Artificial neural networks: PHI Learning Pvt. Ltd.

Classification approaches for automatic speech recognition system Abdel-Hamid, O. , Mohamed, A. R. , Jiang, H. , Deng, L. , Penn, G. , & Yu, D. (2014). Convolutional NeuralNetworks for Speech Recognition. IEEE-ACM Transactions on Audio Speech and Language Processing,22(10), 1533–1545. doi:10.1109/Taslp.2014.2339736 Allen, J. B. (1995). How do humans process and recognize speech? In Modern methods of speechprocessing (pp. 251–275): Springer. Amodei, D. , Ananthanarayanan, S. , Anubhai, R. , Bai, J. , Battenberg, E. , Case, C. , … Chen, G. (2016).Deep speech 2: End-to-end speech recognition in English and Mandarin. Paper presented at theInternational Conference on Machine Learning. Baggenstoss, P. M. (2001). A modified Baum-Welch algorithm for hidden Markov models with multipleobservation spaces. IEEE transactions on speech and audio processing, 9(4), 411–416. Chen, X. , Liu, X. , Gales, M. J. , & Woodland, P. C. (2015). Recurrent neural network language modeltraining with noise contrastive estimation for speech recognition. Cyran, K. A. , Kozielski, S. , Peters, J. F. , Stanczyk, U. , & Wakulicz-Deja, A. (2009). Man-MachineInteractions (Vol. 59): Springer Science & Business Media. Dua, M. , Aggarwal, R. K. , & Biswas, M. (2018). GFCC based discriminatively trained noise robustcontinuous ASR system for Hindi language. Journal of Ambient Intelligence and Humanized Computing,1–14. Fauziya, F. , & Nijhawan, G. (2014). A Comparative study of phoneme recognition using GMM-HMM andANN based acoustic modeling. International Journal of Computer Applications, 98(6). Fukunaga, K. (1993). Statistical pattern recognition. In Handbook of pattern recognition and computer vision(pp. 33–60): World Scientific. Gales, M. , & Young, S. (2008). The application of hidden Markov models in speech recognition.Foundations and Trends® in Signal Processing, 1(3), 195–304. Hermansky, H. (1990). Perceptual linear predictive (PLP) analysis of speech. The Journal of the AcousticalSociety of America, 87(4), 1738–1752. Hermansky, H. , & Morgan, N. (1994). RASTA processing of speech. IEEE transactions on speech andaudio processing, 2(4), 578–589. Hinton, G. , Deng, L. , Yu, D. , Dahl, G. E. , Mohamed, A.-R. , Jaitly, N. , … Sainath, T. N. (2012). Deepneural networks for acoustic modeling in speech recognition: The shared views of four research groups.IEEE Signal processing magazine, 29(6), 82–97. Jiang, H. , Li, X. , & Liu, C. (2006). Large margin hidden Markov models for speech recognition. IEEEtransactions on audio, speech, and language processing, 14(5), 1584–1595. Jozefowicz, R. , Vinyals, O. , Schuster, M. , Shazeer, N. , & Wu, Y. (2016). Exploring the limits of languagemodeling. arXiv preprint arXiv:1602.02410. Liu, Y. , & Fung, P. (2004). State-dependent phonetic tied mixtures with pronunciation modeling forspontaneous speech recognition. IEEE transactions on speech and audio processing, 12(4), 351–364. Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561–580. Masmoudi, S. , Frikha, M. , Chtourou, M. , & Hamida, A. B. (2011). Efficient MLP constructive trainingalgorithm using a neuron recruiting approach for isolated word recognition system. International Journal ofSpeech Technology, 14(1), 1–10. Mohamed, A.-R. , Dahl, G. , & Hinton, G. (2009). Deep belief networks for phone recognition. Paperpresented at the Nips workshop on deep learning for speech recognition and related applications. Mohamed, A. R. , Dahl, G. E. , & Hinton, G. (2012). Acoustic Modeling Using Deep Belief Networks. IEEETransactions on Audio Speech and Language Processing, 20(1), 14–22. doi:10.1109/Tasl.2011.2109382 Müller, M. (2007). Dynamic time warping. Information retrieval for music and motion, 69–84. O’Shaughnessy, D. (2008). Automatic speech recognition: History, methods and challenges. PatternRecognition, 41(10), 2965–2979. Passricha, V. , & Aggarwal, R. K. (2019a). End-to-End Acoustic Modeling Using Convolutional NeuralNetworks. In Intelligent Speech Signal Processing (pp. 5–37): Elsevier. Passricha, V. , & Aggarwal, R. K. (2019b). PSO-based optimized CNN for Hindi ASR. International Journal ofSpeech Technology, 22(4), 1123–1133. Renjith, S. , & Manju, K. G. (2017, 20–21 April 2017). Speech based emotion recognition in Tamil andTelugu using LPCC and hurst parameters—A comparitive study using KNN and ANN classifiers. Paperpresented at the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT). Senior, A. , Heigold, G. , Bacchiani, M. , & Liao, H. (2014). GMM-free DNN acoustic model training. Paperpresented at the Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conferenceon. Siniscalchi, S. M. , & Lee, C.-H. (2009). A study on integrating acoustic-phonetic information into latticerescoring for automatic speech recognition. Speech communication, 51(11), 1139–1153.

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SIM-BERT: Speech intelligence model using NLP-BERT with improved accuracy Berard, A. (2016). Listen and translate: A proof of concept for end-to-end speech-to-text translation, NIPS. Lee, H. , and Glass, J. (2015). IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(9),pp. 1389–1420. Chen , (2017). Order preserving abstractive summarization for spoken content based on connectionisttemporal classification, Inter-speech 2017, pp. 2899–2903, 2017. Serdyuk, D. (2018). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).IEEE, 2018, pp. 5754–5758. Chuang, G. (2018) Approaches to end-to-end spoken language understanding, IEEE (SLT). IEEE, 2018, pp.720–726. Devlin, J. (2019). Bert: Pretraining of deep bidirectional transformers for language understanding: HumanLanguage Technologies, vol. 1 (Long and Short Papers), pp. 4171–4186. Radford, A. (2018). Improving language understanding by generative pre-training. Rajpurkar, P. (2016). Conference on Empirical Methods in Natural Language Processing, 2016, pp.2383–2392. Rajpurkar, P. (2018). 56th Annual Meeting of the Association for Computational Linguistics, vol. 2 (ShortPapers), 2018, pp. 784–789. Chung , (2016), Audio word2vec: Unsupervised learning of audio segment representations using sequence-to-sequence autoencoder, Interspeech 2016, pp. 765–769.

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A literature review on virtual assistant for visually impaired Iyer, V. , Shah, K. , Sheth, S. , and Devadkar, K. (2020, June). Virtual assistant for the visually impaired. In:2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 1057–1062).IEEE. Khairnar, D.P. , Karad, R.B. , Kapse, A. , Kale, G. , and Jadhav, P. (2020, April). Partha: A visually impairedassistance system. In: 2020 3rd International Conference on Communication System, Computing and ITApplications (CSCITA) (pp. 32–37). IEEE. Khaled, N. , Mohsen, S. , El-Din, K.E. , Akram, S. , Metawie, H. , and Mohamed, A. (2020, June). In-DoorAssistant Mobile Application Using CNN and TensorFlow. In: 2020 International Conference on Electrical,Communication, and Computer Engineering (ICECCE) (pp. 1–6). IEEE. Kukade, Rutuja V. , Fengse, Ruchita G. , Rodge, Kiran D. , Ransing, Siddhi P. , Lomte, Vina M. (2018).Virtual Personal Assistant for the Blind, Dept. of Computer Engineering, RMD Sinhgad School ofEngineering, Pune, Maharashtra, India. Matusiak, K. , Skulimowski, P. , and Strurniłło, P. (2013, June). Object recognition in a mobile phoneapplication for visually impaired users. In: 2013 6th International Conference on Human System Interactions(HSI) (pp. 479–484). IEEE. Mishra, R. , Mondal, S. , Kuchnure, A. , and Shah, R. (2016). Directions Aids for the Visually ChallengedUsing Image Processing Using Image Recognition. Singh, G. , Takhtani, K. , Kandale, O. and Dadhwal, N. , 2019. A Smart Personal AI Assistant for VisuallyImpaired People: A Survey. Sultan, M. R. , and Hoque, M. M. (2019, December). ABYS (Always By Your Side): A Virtual Assistant forVisually Impaired Persons. In: 2019 22nd International Conference on Computer and InformationTechnology (ICCIT) (pp. 1–6). IEEE. Tembhurne, N. M. , Vaidya, S. V. , Afrin Shiekh, P. , and Dravyakar, S. (2019). Voice Assistant for VisuallyImpaired People. Vtyurina, A. , Fourney, A. , Morris, M. R. , Findlater, L. and White, R. W. (2019, May). Bridging screenreaders and voice assistants for enhanced eyes-free web search. In: The World Wide Web Conference (pp.3590–3594). Wang, R. , Yu, C. , Yang, X. D. , He, W. and Shi, Y. (2019, May). EarTouch: facilitating smartphone use forvisually impaired people in mobile and public scenarios. In: Proceedings of the 2019 CHI Conference onHuman Factors in Computing Systems (pp. 1–13).

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