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Minimum data generation for Telugu speech recognition

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Abstract

A morphologically rich language has hundreds of forms of each word which makes storing and maintaining them time and resource consuming. It also leads to confusions while recognizing speech which leads to more word error rate. These issues make it difficult to build applications of speech recognition for such languages. Hence there is a need to develop a phonetically balanced minimal data set. This paper describes generating minimum dataset for Telugu language, the second most widely spoken language in India. Considering minimum data generation as a set covering problem, a variety of datasets are generated based on different criteria. From various set covering algorithms, Greedy algorithm is chosen. The criterion used for final data selection is the frequency of occurrence of words. As set covering requires a large set of data from which minimum data is selected, a 15 Million word text corpus has been created. Thorough analysis of this text corpus is carried out in order to ensure that the generated set is phonetically balanced. The generated minimum dataset consists of 21 words and covers each phoneme of the Telugu language. Telugu speech technology researchers can benefit from this data set in building applications of phoneme level speech recognition by reducing manual recording effort and time. This paper discusses the role of minimum data set in LVSR systems, details of the text corpus created and proposed algorithm for minimum data generation.

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Authors and Affiliations

  1. BVRIT Hyderabad College of Engineering for Women, Bachupally, Hyderabad, AP, India

    K. V. N. Sunitha

  2. G. Narayanamma Institute of Technology & Science for Women, Shaikpet, Hyderabad, AP, India

    A. Sharada

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  1. K. V. N. Sunitha
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  2. A. Sharada
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Correspondence to K. V. N. Sunitha.

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Sunitha, K.V.N., Sharada, A. Minimum data generation for Telugu speech recognition. Int J Speech Technol 18, 217–230 (2015). https://doi.org/10.1007/s10772-014-9262-4

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  • DOI: https://doi.org/10.1007/s10772-014-9262-4

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