Implementation of Marathi Language Speech Databases for Large Dictionary (original) (raw)
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Development of indian language speech databases for large vocabulary speech recognition systems
2005
In this paper, we discuss our efforts in the development of Indian language speech databases in Tamil, Telugu and Marathi for building large vocabulary speech recognition systems. We have collected speech data from about 560 speakers in these three languages. We discuss the design and methodology of collection of speech databases. We also present preliminary speech recognition results using the acoustic models created on these databases using Sphinx 2 speech tool kit.
Speech is a natural means of communication between humans. Human being tried to develop computer that can understand & talk like human. Digital content can research to the masses & facilitate the exchange of information across peoples speaking different language in form of natural interface provided by language technologies. Developing certain recognition system standard, speech database is a prerequisite. 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.
Marathi Speech Database Standardization:A Review and Work
Vol. 19 No. 7 JULY 2021 International Journal of Computer Science and Information Security (IJCSIS), 2021
Automatic Speech Recognition System (ASR) is helpful for interaction between human and machine. It is the way to operate computer and mobile phones through speech only, without taking such extra efforts. The term corpus is used for Standardized Database, which contains a collection of audio recordings of spoken language with its annotations and documents. When existing literature was reviewed, it was observed that much literature is available on how to create speech databases. But few literatures are available about the standardization. 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. The main problem in designing of a speech database is to deal with variability of speech. In recent years, there is much need to develop speech corpora for training and testing materials to be used for wide range of applications of speech technology like Linguistic Consortium, Speech interfaces development and language models etc. If it is standardized in regional languages, it will certainly contribute in many applications and research. In future, we would like to work to find standard way to standardized speech databases so with the help of this we can retrieve data easily and more efficiently.
Design and Development of Speech Database for Travel Purpose in Marathi
2014
The paper represents the brief information about developing speech database in Marathi language for Travel purpose in Aurangabad District. Development of speech database is very primary requirement for developing an Automatic Speech Recognition System. The accuracy of speech recognition depends on the quality of the speech data recorded and the algorithms implemented for the development of ASR. The data collection procedure from various speakers from Aurangabad district is described in the paper for developing ASR system in Marathi language for travel domain.
Indian Language Speech Database: A Review
International Journal of Computer Applications, 2012
Speech is the most prominent and natural form of communication between humans. Human beings have long been motivated to create computer that can understand and talk like human. When the research tries to develop certain recognition system they require certain previously stored data i.e. database for respective recognition system. 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.
IOSR Journal of Computer Engineering, 2014
The paper represents the brief information about developing speech database in Marathi language for Travel purpose in Aurangabad District. Development of speech database is very primary requirement for developing an Automatic Speech Recognition System. The accuracy of speech recognition depends on the quality of the speech data recorded and the algorithms implemented for the development of ASR. The data collection procedure from various speakers from Aurangabad district is described in the paper for developing ASR system in Marathi language for travel domain.
Recent Advances of Speech Databases Development Activity for Indian Languages
Proc. of ISCSLP, 2006
Development of Speech Corpora and acoustic-phonetic data bases are indispensable for any research and development work in spoken language systems. Systematic efforts have been made to create speech databases for some major languages of India. The paper attempts to present the status and the recent advancements made in corpora development for some of the Indian languages. Different types of databases developed include text corpora for speech, annotated/non-annotated speech corpora, acoustic-phonetic and labeled speech databases, special speech corpora etc. These have been developed for general purpose as well as task oriented applications. Databases of a few Indian languages have been developed in a well designed manner which includes adequate representation of textual / linguistic information, regional/dialectal variations, speaking styles and environments etc. These databases have been used for developing systems such as Text to Speech synthesis, Speech recognition, Speaker identification, speech secrecy, language translation and forensic applications etc.
Marathi Isolated Words Speech Database for Agriculture Purpose
The research in the domain of the language technologies for Indian languages is far behind than the languages of developed nation. The work for the Indo-Aryan language, i.e. Marathi is behind. Development of speech database is the basic need for developing an automatic speech recognition system. The accuracy of speech recognition depends on the quality of the speech data collected and the quality of training set data. This paper describes the progress in the development of isolated words Speech database of Marathi language for agriculture purpose.
Design and Development of Speech Database of Marathi Numerals
This paper describes the approach followed for development of speech database of Marathi digits starting from Shunya (zero) up to Nau (nine). The following paper describes the step by step procedure followed for the development of the speech database. For the development of automatic speech recognition (ASR) it is necessary to have a speech databases and the recognition rate depends upon the quality of the used speech databases. I. INTRODUCTION Speech is the way communication between humans where human can share their information with each other. The researchers around the world are trying to develop new interface system for communication between human and computer. Speech is having the capability of being used as a mode of interaction between human and Computer. Estimated number of languages spoken around the world varies between 6,000 and 7,000. Language technologies can play a vital role in the natural interfaces for those who can't understand the particular language. The lan...
—Speech is one of the easiest and the fastest way to communicate. Recognition of speech by computer for various languages is a challenging task. The accuracy of Automatic speech recognition system (ASR) remains one of the key challenges, even after years of research. Accuracy varies due to speaker and language variability, vocabulary size and noise. Also, due to the design of speech recognition that is based on issues like-speech database, feature extraction techniques and performance evaluation. This paper aims to describe the development of a speaker-independent isolated automatic speech recognition system for Indian English language. The acoustic model is build using Carnegie Mellon University (CMU) Sphinx tools. The corpus used is based on Most Commonly used English words in everyday life. Speech database includes the recordings of 76 Punjabi Speakers (northwest Indian English accent). After testing, the system obtained an accuracy of 85.20 %, when trained using 128 GMMs (Gaussian Mixture Models).