Maggie Mi
The aim of this project is to explore the classification performance of an automatic language identification (LID) system. Such a system attempts to identify a language from a series of randomly spoken utterances.
In the current world that is inundated by the abundance of data, LID systems provide the foundations of multimedia mining systems, spoken-document retrieval, as well as multilingual spoken dialog systems. The idiosyncratic nature of natural languages means “rule-based” systems are insufficient approaches to model languages. Thus, a visible challenge is structuring what seems to be highly unstructured datasets. The use of probability is significant in natural language processing (NLP), as quantitative techniques can account for such idiosyncrasies. The approach taken in this paper involves building an acoustic model that uses probabilistic representations of the speech datasets across 10 different languages (Dutch, Russian, Italian, Spanish, Portuguese, German, English, French, Turkish, and Greek). Each language is modelled using Gaussian Mixture Models (GMMs). Sets of unseen speech data groupings are then passed through the system for identification. Notably, exploring the performance of such GMMs reveals areas of weakness and corresponding means of improvements.
Maggie Mi
Hi there, welcome to my webpage. I will use the space below to tell you about my Independent Study project. Let us start with a quick introduction.
Background of The Study
Data Preparation
Taken from Gales & Young, 2008, p. 21
Building the model using HTK (Hidden Markov Toolkit)
Research Questions
Results and Analysis
Discussion and Conclusion
Conclusion
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