Connected Bangla Speech Recognition using Artificial Neural Network

Ahammad, Khalil; Rahman, Md. Mahfuzur

doi:10.5120/ijca2016911568

Cited by 12 publications

(6 citation statements)

References 4 publications

(3 reference statements)

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“…The general and most used preprocessing techniques on speech data in Natural Language processing are Noise Removal [118] [119], Pre-emphasis [120] [4], Hamming Window [2], Segmentation [50], Sampling [121] Removal [122], Voice Activity Detection [123], Framing [124] and many more.…”

Section: A Necessity and Needsmentioning

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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Section: A Necessity and Needsmentioning

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 general and most used preprocessing techniques on speech data in Natural Language processing are Noise Removal [114] [115], Pre-emphasis [116] [4], Hamming Window [2], Segmentation [49], Sampling [117] [5], Phoneme Mapping [52], Speech Coding [4], Stemming without Noise Removal [118], Voice Activity Detection [119], Framing [120] and many more.…”

Section: B Preprocessingmentioning

confidence: 99%

“…In 2017, Ahammad et al [49] proposed a connected digit recognition system based on neural network for the Bangla language. For training and testing purposes, the authors proposed two datasets of connected digits comprising fifteen male and fifteen female speakers.…”

Section: ) Machine Learning Approachesmentioning

confidence: 99%

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

Sen,

Fuad,

Islam

et al. 2021

Preprint

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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 study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories,

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“…In 2016, Khalil and Rahman developed an ANN based approach for recognizing Bangla speech [6]. MFCC analysis is used here to elicit meaningful features for recognition.…”

Section: A Speech Recognitionmentioning

confidence: 99%

A Digital Personal Assistant using Bangla Voice Command Recognition and Face Detection

Gupta

Hossain

et al. 2019

2019 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON)

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Though speech recognition has been a common interest of researchers over the last couple of decades, but very few works have been done on Bangla voice recognition. In this research, we developed a digital personal assistant for handicapped people which recognizes continuous Bangla voice commands. We employed the cross-correlation technique which compares the energy of Bangla voice commands with prerecorded reference signals. After recognizing a Bangla command, it executes a task specified by that command. Mouse cursor can also be controlled using the facial movement of a user. We validated our model in three different environments (noisy, moderate and noiseless) so that the model can act naturally. We also compared our proposed model with a combined model of MFCC & DTW, and another model which combines crosscorrelation with LPC. Results indicate that the proposed model achieves a huge accuracy and smaller response time comparing to the other two techniques.

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Connected Bangla Speech Recognition using Artificial Neural Network

Cited by 12 publications

References 4 publications

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

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

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

A Digital Personal Assistant using Bangla Voice Command Recognition and Face Detection

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