Bangla Speech-to-Text conversion using SAPI

Sultana, Shaheena; Akhand, M. A. H.; Das, Prodip Kumer; Rahman, M.M. Hafizur

doi:10.1109/iccce.2012.6271216

Cited by 32 publications

(6 citation statements)

References 2 publications

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“…Then the matched word fetched the Bangla words form the database. The experimental recognition rate was nearly 78% on average [9].…”

Section: Speech-to-text (Stt)mentioning

confidence: 82%

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A Sophisticated HCI Perspective: Advanced Bengali Phonetics Communication System for Disabled (Deaf and Dumb) Persons

Sakib¹,

Amlan²,

Islam³

et al. 2017

IJCA

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The purpose of this study is to develop an intelligent system that can help the people who are dumb and deaf as they are recognized as disabled persons. These people face difficulties in communicating with other people in their regular life activities such as travelling, shopping etc. Nowadays, Human Computer Interaction (HCI) domain facing and solving the problem of disabled persons of this day to day activities. In this paper a new solution of this problem and the perspective of HCI is being focused. This paper is synthesized and analyzed speech-to-text (STT) and text-to-speech (TTS) algorithms to implement the system for those people who are deaf & dumb. The system will behave and act on behalf of the person and it will also capture the words from the other side and convert it to text, thus the deaf person may understand others say through the display of this system. There is a huge amount of people who are deaf and dumb in Bangladesh. Therefore, the system has been implemented both in Bengali phonetics & English language. The performance of this paper showed a satisfactory result of this system.

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“…Then the matched word fetched the Bangla words form the database. The experimental recognition rate was nearly 78% on average [9].…”

Section: Speech-to-text (Stt)mentioning

confidence: 82%

“…A conversion method can be developed from speech to text using SAPI for Bangla language [9]. SAPI is Speech Application Program Interface developed by Microsoft Corporation which is used for speech related works in windows operating system.…”

Section: Speech-to-text (Stt)mentioning

confidence: 99%

A Sophisticated HCI Perspective: Advanced Bengali Phonetics Communication System for Disabled (Deaf and Dumb) Persons

Sakib¹,

Amlan²,

Islam³

et al. 2017

IJCA

View full text Add to dashboard Cite

show abstract

“…The most commonly used classical methods in speech processing and recognition system are Dynamic Time Warping (DTW) [2], Hidden Markov Model (HMM) [3], Linear Predictive Coding (LPC) [4], Gaussian Mixture Model (GMM) [2], Template Matching [2], Autocorrelation [5], Cepstrum [5], Speech Application Program Interface (SAPI) [6], Factorial Hidden Markov Model [7], Minimum Classification Error [8], Knowledge Based Approaches [9], Template Based Approaches [9], and Perceptual Linear Prediction [10].…”

Section: B Classical Methodsmentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

et al. 2022

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The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive review of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP research papers and categorize them into 11 categories,

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“…[3] Some work on recognizing Bangla has been done by performing some modifications in Microsoft's SAPI API. [8] This approach of using training data opens up various challenges, chiefly the one of storing all this data and writing a computer program to use it in order to recognize speech. Also, the time required to train a large vocabulary system is a lot, which alleviates the need for creation of speaker independent speech recognition systems.…”

Section: Previous Workmentioning

confidence: 99%

Automatic recognition of class variants of Marathi consonants

Deshmukh

Laulkar

Rajankar

2015

2015 International Conference on Pervasive Computing (ICPC)

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Marathi is a language spoken predominantly in the state of Maharashtra, India and has close to 70 million native speakers. It is based on the Devanagiri script, which the script used for the mother of all Indic languages, Sanskrit. Devanagiri consists of 46 phonemes and arranges them in a scientific manner based on the process of generation. The consonants are arranged in varnas (classes), and each varna has a unique place of articulation. This work focuses on identifying individual class variants of the first five varnas of the Marathi Devanagiri script.

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Bangla Speech-to-Text conversion using SAPI

Cited by 32 publications

References 2 publications

A Sophisticated HCI Perspective: Advanced Bengali Phonetics Communication System for Disabled (Deaf and Dumb) Persons

A Sophisticated HCI Perspective: Advanced Bengali Phonetics Communication System for Disabled (Deaf and Dumb) Persons

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

Automatic recognition of class variants of Marathi consonants

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