Abstract
This paper presents a brief study of remarkable works done for the development of Automatic Speech Recognition (ASR) system for Bangla language. It discusses information of available speech corpora for this language and reports major contributions made in this research paradigm in the last decade. Some important design issues to develop a speech recognizer are: levels of recognition, vocabulary size, speaker dependency and approaches for classifications; these have been defined in this paper in the order of complexity of speech recognition. It also highlights on some challenges which are very important to resolve in this exciting research field. Different studies carried out on last decade for Bangla speech recognition have been shortly reviewed in a chronological order. It was found that selection of classification model and training dataset play important roles in speech recognition.
Highlights
There are several important applications of a speech recognition system
The aim of this paper is to summarise all important works done recently on the development of Bangla Automatic Speech Recognition (ASR) to facilitate the researchers working in this filed
Recent researches focus on speech recognition based on Deep neural network (DNN), Recurrent neural network (RNN), hybrid of Hidden Markov Model (HMM)-DNN approaches
Summary
There are several important applications of a speech recognition system. It is used to develop chat-bots in smartphones and gadgets. A study shows that for the English language more than 10% of searches are made by voice and most of them are done using smartphones [2]. This number will increase day by day. Google presented a functional speech recognizer and voice search service (SpeechTexter and Google Assistant) for Bangla and other languages. These are available for only android devices.
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