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Hamro Awaaz: An Automated Speech Recognizer for Nepali Language 

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Resource details

Resource ID

220

Access

Open

Contributed by

Rajan Karmacharya

Author/Contributor

Pooja Kumari Jha, Sheetal Giri, Rajan Karmacharya (Supervisor)

Publisher/Credit

St. Xavier's College

Date

08 July 16

Document type

Thesis or project

Department

Computer science

Level

Bachelor

Keywords

Recurrent Neural Network, Connectionist Temporal Classification (CTC), N-gram
Language Model, Automatic Speech Recognition

Abstract

Speech recognition is the process of enabling a computer to identify and respond to the
sounds produced in human speech. Hamro Awaaz - Nepali Automated Speech
Recognizer (ASR) performs the speaker-independent, computer‚Äźdriven transcription of
spoken Nepali into readable Devanagari text in real time.
The project is based around an android application through which user will send their
voice recording to the server, where it is processed to corresponding text and responded
back. The base of any speech recognition system is its acoustic and language model.
These models in turn are dependent on the amount and quality of data collected, and the
training algorithms. A deep recurrent neural network with Connectionist Temporal
Classification (CTC) in the output layer is being used for training acoustic model.
Language modeling is done by means of n-gram distribution among data collected.
Finally, a search algorithm is used to find the best matching transcription to user's speech
input.
Through these techniques we will be able to achieve a speech recognition model that can
be helpful to all seeking better model of speech recognition for Nepali and other
languages as well. The resulting application can create a platform for development of
other Nepali voice based applications.

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