Speaker Dependent ASRs for Huastec and Western-Huastec Náhuatl Languages

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3523)


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.


Automatic Speech Recognition Minority Language Speech Recognition System Word Error Rate Automatic Speech Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Departamento de Ciencias ComputacionalesITESM, Campus MonterreyMonterreyMéxico

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