Download Computer and Robot Vision (Volume 1) by Robert M. Haralick PDF

By Robert M. Haralick

ISBN-10: 0201108771

ISBN-13: 9780201108774

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Additional resources for Computer and Robot Vision (Volume 1)

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4 Reasons for Attraction Towards Implicit LID Systems 21 approach is the availability of phone recognizers of all the languages to be identified. To develop a phone recognizer for any language, a segmented and labeled speech corpus is necessary. Building segmented and labeled speech corpora for all the languages to be recognized, is both time consuming and expensive, requiring trained human annotators and substantial amount of supervision [76]. The other approaches tried in [11], do not require segmented and labeled speech corpora for all the languages to be recognized.

Koolagudi SG, Rastogi D, Sreenivasa Rao K (2012) Spoken language identification using spectral features. Communications in computer and information science (CCIS): contemporary computing, vol 306. Springer, New York, pp 496–497 76. Greenberg S (1999) Speaking in short hand–a syllable-centric perspective for understanding pronunciation variation. Speech Comm 29:159–176 77. Maity S, Vuppala AK, Rao KS, Nandi D (2012) IITKGP-MLILSC speech database for language identification. In: National Conference Communication, Feb 2012 78.

Each row corresponds to classification accuracy of a particular language test samples. For example, first row indicates the classification accuracy of “Arunachali”. It shows that 77 % of Arunachali test samples are correctly classified, and the remaining 23 % samples are misclassified as Dogri (with 1 %) and Sanskrit (with 22 %). From the confusion matrix it is observed that each language is mostly misclassified (confused) into 4–6 other languages. Hence, the most of the entries in the confusion matrix are found to be zero.

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Computer and Robot Vision (Volume 1) by Robert M. Haralick


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