Performance and Evaluation

Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

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).

Keywords

Clarification 

References

  1. 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 96Google Scholar
  2. Jelinek F (1999) Statistical methods for speech recognition, Language, speech and communication series. MIT, Cambridge, MAGoogle Scholar
  3. Jurafsky D, Martin J (2009) Speech and language processing, 2nd edn. Pearson, NJGoogle Scholar
  4. Plötz T (2005) Advanced stochastic protein sequence analysis, Ph.D. thesis, Bielefeld UniversityGoogle Scholar
  5. Saon G, Padmanabhan M (2001) Data-driven approach to designing compound words for continuous speech recognition. IEEE Trans Speech Audio Process 9(4):327–332CrossRefGoogle Scholar

Copyright information

© Dia AbuZeina 2012

Authors and Affiliations

  1. 1.King Fahd University of Petroleum and MineralsDhahranSaudi Arabia

Personalised recommendations