Solomon Teferra Abate

Syllable-Based Speech Recognition for Amharic

Amharic is the Semitic language that has the second large number of speakers after Arabic (Hayward and Richard 1999). Its writing system is syllabic with Consonant-Vowel (CV) syllable structure. Amharic orthography has more or less a one to one correspondence with syllabic sounds. We have used this feature of Amharic to develop a CV syllable-based speech recognizer, using Hidden Markov Modeling (HMM), and achieved 90.43% word recognition accuracy.

Morpheme-based and factored language modeling for amharic speech recognition

This paper presents the application of morpheme-based and factored language models in an Amharic speech recognition task. Since the use of morphemes in both acoustic and language models often results in performance degradation due to a higher acoustic confusability and since it is problematic to use factored language models in standard word decoders, we applied the models in a lattice rescoring framework. Lattices of 100 best alternatives for each test sentence of the 5k development test set have been generated using a baseline speech recognizer with a word-based backoff bigram language model. The lattices have then been rescored by means of various morpheme-based and factored language models. A slight improvement in word recognition accuracy has been observed with morpheme-based language models while factored language models led to notable improvements in word recognition accuracy.