Abstract
The purpose of this work is to show the results obtained when the latest technological advances in the area of Automatic Speech Recognition (ASR) are applied to the Western-Huastec Náhuatl and Huastec languages. Western-Huastec Náhuatl and Huastec are not only native (indigenous) languages in México, but also minority languages, and people who speak these languages usually are analphabetic. A speech database was created by recording the voice of native speaker when reading a set of documents used for native bilingual primary school in the official mexican state education system. A pronunciation dictionary was created for each language. A continuous Hidden Markov Models (HMM) were used for acoustical modeling, and bigrams were used for language Modeling. A Viterbi decoder was used for recognition. The word error rate of this task is below 8.621% for Western-Huastec Náhuatl language and 10.154% for Huastec language.
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Nolazco-Flores, J.A., Salgado-Garza, L.R., Peña-Díaz, M. (2005). Speaker Dependent ASRs for Huastec and Western-Huastec Náhuatl Languages. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_73
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DOI: https://doi.org/10.1007/11492542_73
Publisher Name: Springer, Berlin, Heidelberg
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