Voice Recognition for English Language Pattern Recognition Approach

Authors

  • Asad Ullah School of Electronics and Information, Northwestern Polytechnical University ADD:127 West Youyi Road,Xi’an Shaanxi,710072,P.R.China
  • Lu Xuan Min Associate Professor at School of Electronics and Information, Northwestern Polytechnical University ADD:127 West Youyi Road,Xi’an Shaanxi,710072,P.R.China

Keywords:

English Language model, Hidden Markov Model, Feature Extraction, acoustic Model Introduction.

Abstract

Communication is the basic need of everyone for a person who want to survive in this world. Researcher are very focusing from last couple of decades in the area of speech recognition. Some peoples cannot communicate in the proper way just like physically hampered and color blind etc. For this purpose a lot of researcher and mankind people working on automatic speech recognition. ASR also sometime call computer speech recognition. In this modern world time is one of the most important factor to be saved as much as possible. Due to a lot of computer software and advancement in a science provided, now we are capable to process a lot of software which were impossible to access before few years.

In this paper we are going to discuss something about voice recognition through different feature like HMM (Hidden Markov Model), acoustic model etc. In this paper we will focused on accuracy because accuracy is the basic key factor in speech recognition. Every time environment change, speaker is not always be fixed person, there is also variation in context occurs and also how to maximize the size of vocabulary will be our aim and goal that will be discussed in this review paper.

References

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Published

2016-02-27

How to Cite

Ullah, A., & Min, L. X. (2016). Voice Recognition for English Language Pattern Recognition Approach. American Scientific Research Journal for Engineering, Technology, and Sciences, 17(1), 95–104. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/1181

Issue

Vol. 17 No. 1 (2016)

Section

Articles

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