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Speech Corpus Development for Speaker Independent Speech Recognition for Indian Languages

Amaresh Kandagal, V Udayashankara
unpublished
In this paper, we discuss development of speech corpus for speaker independent speech recognition for Indian airports and it is extended for continuous speech recognition Indian languages.  ...  We also discuss preliminary isolated speech recognition results using the acoustic models created on these corpus using Hidden Markov Model toolkit (HTK).  ...  Acknowledgements This work has been conducted through the projects, automatic airline ticket booking system and immersive languages learning tools for Indian languages at Sprec India Pvt Ltd and Focus  ... 
fatcat:lreeu2j4ujhsvc6z7jk5xtevm4

A Review on Speech Corpus Development for Automatic Speech Recognition in Indian Languages

Cini Kurian
2014 unpublished
In this paper we review the efforts made in Indian languages for developing speech corpus for automatic speech recognition.  ...  Corpus based Language research has an innovative outlook which will discard the aged linguistic theories. Speech corpus is the essential resources for building a speech recognizer.  ...  This paper review the development of speech corpus in Indian languages II.SURVEY ON SPEECH CORPUS OF INDIAN LANGUAGES Speech corpus has been collected in Marathi language at TIFR (Mumbai) and IIT Bombay  ... 
fatcat:k6ydfpebcfamjooeslstfwncka

Development of IIITH Hindi-English Code Mixed Speech Database

Banothu Rambabu, Suryakanth V Gangashetty
2018 The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages  
Since computers can recognize Roman symbols, we used Indian Language Speech Sound Label (ILSL) transcription.  ...  An acoustic model is built for Hindi-English mixed language instead of language-dependent models.  ...  The authors would like to thank the internship students namely Rohit kumar, Sai Teja, Sumedh, Bhavana, Sai, Nikhil and students of IIITH, for their help in speech recording and spending their time in correcting  ... 
doi:10.21437/sltu.2018-23 dblp:conf/sltu/RambabuG18 fatcat:k33niojkrfaztie5ekkw5hynny

Computational intelligence in processing of speech acoustics: a survey

Amitoj Singh, Navkiran Kaur, Vinay Kukreja, Virender Kadyan, Munish Kumar
2022 Complex & Intelligent Systems  
This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics.  ...  This paper examined major challenges for speech recognition for different languages.  ...  Issues in the construction of speech corpus were observed and studied [160] for Indian languages.  ... 
doi:10.1007/s40747-022-00665-1 fatcat:6pu2xccbq5as7bn2y2tav2fdwa

Marathi Speech Database Standardization: A Review and Work

Sonal A.Tiwari, Rajashri G. Kanke, Maheshwari A. Ambewadikar, Manasi R. Baheti
2021 Zenodo  
Such work is done for the languages other than Indian languages. But for the Hindi, Marathi etc., standardization for the speech datasets is not up to the mark.  ...  Abstract---Automatic Speech Recognition System (ASR) is helpful for interaction between human and machine.  ...  This database developed for large vocabulary speech Recognition systems. CIIL (Central Institute of Indian Languages) corpus of Marathi language was used for text corpus collection [5] .  ... 
doi:10.5281/zenodo.5501909 fatcat:egpdoyckjvczxp264goeni233e

Resources for Development of Hindi Speech Synthesis System: An Overview

Archana Balyan
2017 Open Journal of Applied Sciences  
Most of the information in digital world is accessible to few who can read or understand a particular language. The speech corpus acquisition is an essential part of all spoken technology systems.  ...  The quality and the volume of speech data in corpus directly affect the accuracy of the system.  ...  Introduction The objective of speech data collection is to primarily build speech recognition and synthesis systems for Indian languages [1] .  ... 
doi:10.4236/ojapps.2017.76020 fatcat:74qrju5ex5gu7aikudvoj237lq

Indian Language Speech Database: A Review

Pukhraj P.Shrishrimal, Ratnadeep R. Deshmukh, Vishal B. Waghmare
2012 International Journal of Computer Applications  
There are various speech databases available for European Language but very less for Indian Language.  ...  In this paper we discuss the various Speech Database developed in different Indian Languages for speech recognition system & Text to Speech System.  ...  At HP Labs India Speech Recognition for various Indian languages is going on.  ... 
doi:10.5120/7184-9893 fatcat:pjdpjihfvbblrfmcqrn4owl6i4

Web Recognition of Spoken Hindi

Kamlesh Sharma, Suryakanthi Tangirala
2017 Indian Journal of Science and Technology  
Interviews were conducted uto record speech files from users for developing the speech database for training the system. The system takes spoken Hindi as input and generates Hindi sentences.  ...  The dictionary maintains the POS of source to target language and CFG grammar based model for Hindi language was developed.  ...  The corpus then is made to read out by a native speaker of Hindi and is recorded to produce the speech corpus for Hindi language.  ... 
doi:10.17485/ijst/2017/v10i35/118956 fatcat:ro2o2r6noza6fjl4d35rcml5wi

Issues in developing LVCSR System for Dravidian Languages: An Exhaustive Case Study for Tamil

Bharadwaja KumarG, Melvin Jose Johnson Premkumar
2013 International Journal of Computer Applications  
Research in the area of Large Vocabulary Continuous Speech Recognition (LVCSR) for Indian languages has not seen the level of advancement as in English since there is a dearth of large scale speech and  ...  We have shown the impact of important parameters such as absolute beam width, language weight, number of gaussians and the number of senones on speech recognition accuracy for limited vocabulary (3k).  ...  INTRODUCTION Recently there is a growing interest in ASR for Indian languages. Initial work on large vocabulary speech recognition started with Hindi in early years of the previous decade.  ... 
doi:10.5120/12172-8180 fatcat:vs7ojmx3wnd4tijawevbdnk6ie

A Review on Marathi Language Speech Database Development for Automatic Speech Recognition (ASR) System

Mrs. Chhaya S. Patil
2017 International Journal of Engineering Research and Applications  
There is lot of scope to develop automatic speech recognition (ASR) system using Indian languages which are of different variations.  ...  This paper present review on speech database developed for Marathi language.  ...  This database developed for large vocabulary speech Recognition systems. CIIL corpus of Marathi language was used for text corpus collection [12] .  ... 
doi:10.9790/9622-0703053436 fatcat:3cwy5vvwsra5xbefy43eqmgsia

Development of Kannada Speech Corpus for Continuous Speech Recognition

Anand H., D. S.
2018 International Journal of Computer Applications  
The paper presents, development of Kannada speech corpus for speaker independent continuous speech recognition.  ...  Speech corpus plays a key role in construction of Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis.  ...  In this paper the development of speech database in kannada language for building large continuous speech recognition system is presented.  ... 
doi:10.5120/ijca2018917255 fatcat:gvxq34mdfffevkakw6nfs37e54

Mixed Language Speech Recognition without Explicit Identification of Language

Kiran Bhuvanagirir, Sunil Kumar Kopparapu
2012 American Journal of Signal Processing  
Ho wever mach ine recognition of mixed language spoken speech is a challenge to a conventional speech recognition engine. There are studies on how to enable recognition of mixed language speech.  ...  followed by language dependent speech recognition engines to do the recognition.  ...  The use of a modified PL enables us (a) avoid building an AM for the mixed language (note that mixed language speech corpus is difficult to collect) and (b) further recognition can be performed with ASR  ... 
doi:10.5923/j.ajsp.20120205.02 fatcat:2g5g7av2ube55bbysneq6xzqxi

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Ravindra Parshuram Bachate, Ashok Sharma, Amar Singh, Ayman A. Aly, Abdulaziz H. Alghtani, Dac-Nhuong Le
2022 Computer systems science and engineering  
This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network (RNN).  ...  The optimized RNN classifier is used for speech recognition after completing the feature extraction task, where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm  ...  For Indian regional languages like Marathi, the Speech Recognition model is a broader research area.  ... 
doi:10.32604/csse.2022.024214 fatcat:6ujh2pue3bgerlorwbtrl73d6y

Segmental and Supra Segmental Feature Based Speech Recognition System for Under Resourced Languages

Tanmay Bhowmik, Shyamal Kumar Das Mandal
2018 The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages  
This paper describes the development of a continuous speech recognition system for Assamese, an under-resourced language of North-East India.  ...  The Speech corpus used in this work consists of 5658 spoken utterances collected from 27 speakers over telephone channel.  ...  But only a few major Indian languages have been explored for the development of speech recognition systems.  ... 
doi:10.21437/sltu.2018-46 dblp:conf/sltu/DekaNS18 fatcat:uil42kik7bgidodiydfzpuepfi

Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages [article]

Spandan Dey, Goutam Saha, Md Sahidullah
2021 arXiv   pre-print
Cross-corpora evaluation was not explored much in LID research, especially for the Indian languages.  ...  We have selected three Indian spoken language corpora: IIITH-ILSC, LDC South Asian, and IITKGP-MLILSC.  ...  In this work, we have conducted one of the very first crosscorpora performance analyses for spoken language recognition with three standard speech corpora in Indian languages.  ... 
arXiv:2105.04639v2 fatcat:hkiho42hvrhi7o24dyj4h7pgfq
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