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Speech recognition system play an essential role in every human being life. It is a software that allows the user to interact with their mobile phones through speech. Speech recognition software splitting down the audio of a speech into various sound waves forms, analyzing each sound form, using various algorithms to find the most appropriate word...
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Knowing the language of an input text/audio is a necessary first step for using almost every natural language processing (NLP) tool such as taggers, parsers, or translation systems. Language identification is a well-studied problem, sometimes even considered solved; in reality, most of the world's 7000 languages are not supported by current systems...
Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for machine translation-related tasks. Among these studies, attention resulted to be a useful source of information to get insights about word alignment also when the input text...
Citations
Speech Recognition is one of the prominent research topics in the field of Natural Language Processing (NLP). The Speech Recognition technique removes the barriers and makes the system ease for inter-communication between human beings and devices. The aim of this study is to analyze the Automatic Speech Recognition System (ASRS) proposed by different researchers using Machine learning and Deep Learning techniques. In this work, Indian and foreign languages speech recognition systems like Hindi, Marathi, Malayalam, Urdu, Sanskrit, Nepali, Kannada, Chinese, Japanese, Arabic, Italian, Turkish, French, and German are considered. An integrated framework is presented and elaborated with recent advancement. The various platform like Hidden Markov Model Toolkit (HMM Toolkit), CMU Sphinx, Kaldi toolkit are explained which is used for building the speech recognition model. Further, some applications are elaborated which depict the uses of ASRS.
In the realm of computer science, a huge number of studies have been done for speech application areas, particularly speech recognition, in the last few decades. Through the process of determining and interpreting, speech recognition enables the system to turn received speech signals into instructions. Speech recognition was not quite as impressive at the time of development or in its initial phases as it is now, so many studies focused on it and made it one of the unique qualities. In the subject of automatic voice recognition, a lot of excellent progress has been made on a range of problems, one of which is determining the speaker's surroundings from the audio around the speaker. The core contribution of this project is to explore how to use extensive speech recognition knowledge to extract the speaker's surrounding environment. Since its inception, the research also gives a brief review of voice recognition techniques and applications.
