Statistical methods for speech recognition
Statistical methods for speech recognition
Techniques for high quality Arabic speech synthesis
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Software engineering: Systems and tools
Extraction of Chinese compound words: an experimental study on a very large corpus
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Data-driven lexicon expansion for Mandarin broadcast news and conversation speech recognition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Automatic tagging of Arabic text: from raw text to base phrase chunks
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Automatic part of speech tagging for Arabic: an experiment using Bigram hidden Markov model
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Cross-word Arabic pronunciation variation modeling for speech recognition
International Journal of Speech Technology
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One major source of suboptimal performance in automatic continuous speech recognition systems is misrecognition of small words. In general, errors resulting from small words are much more than errors resulting from long words. Therefore, compounding some words (small or long) to produce longer words is welcome by speech recognition decoders. In this paper, we present a novel approach to artificially generate compound words using part of speech tagging. For this purpose, we consider two cases in Arabic speech where two words are pronounced without a silence period in between: a noun followed by an adjective, and a preposition followed by any word. To collect the candidate compound words, we use Stanford Arabic tagger to tag all words in our baseline transcription corpus. Then, compound words are generated whenever any of the two cases occur in a sequence of two words. The unique compound words are then added to the expanded pronunciation dictionary, as well as to the language model. Using Sphinx 3, we test the proposed method for a 5.4 hours speech corpus of modern standard Arabic. The results show a significant improvement, as the word error rate is reduced by 2.39%.