Abstract
This chapter presents the results achieved by modeling cross-word pronunciation variation problem of MSA. We practically investigated two MSA phonological rules (Idgham and Iqlaab) which significantly enhanced recognition accuracy. Three ASR’s metrics were measured: word error rate (WER), out of vocabulary (OOV), and perplexity (PP).
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References
Gallwitz F, Noth E, et al (1996) A category based approach for recognition of out-of-vocabulary words. In: Proceedings of fourth international conference on spoken language, 1996. ICSLP 96
Jelinek F (1999) Statistical methods for speech recognition, Language, speech and communication series. MIT, Cambridge, MA
Jurafsky D, Martin J (2009) Speech and language processing, 2nd edn. Pearson, NJ
Plötz T (2005) Advanced stochastic protein sequence analysis, Ph.D. thesis, Bielefeld University
Saon G, Padmanabhan M (2001) Data-driven approach to designing compound words for continuous speech recognition. IEEE Trans Speech Audio Process 9(4):327–332
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© 2012 Dia AbuZeina
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AbuZeina, D., Elshafei, M. (2012). Performance and Evaluation. In: Cross-Word Modeling for Arabic Speech Recognition. SpringerBriefs in Electrical and Computer Engineering(). Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1213-7_6
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DOI: https://doi.org/10.1007/978-1-4614-1213-7_6
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Publisher Name: Springer, Boston, MA
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Online ISBN: 978-1-4614-1213-7
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