HMM-based computer-aided Ilokano language learning (CAILL) system for Tagalog speakers

• Main Authors: Malaay, Emmanual M., Simora, Michael B., Cabatic, Ronald John O., Castillo, Anthony B., Cabotaje, Ayman P., Pascual, Ronald M.
• Format: text
• Published: Animo Repository 2015
• Subjects: Iloko language—Computer-assisted instruction for Tagalog speakers; Computer Sciences; Language and Literacy Education
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/13624
• Institution: De La Salle University

The language learning system employs a speech corpus that contains selected phrases from an Ilokano phrasebook authored by Dr. Carl Rubino [7]. A survey was conducted in Narvacan, Ilocos Sur for the purpose of selecting Ilokano phrases rated as "commonly used". Using the survey results, the speech corpus was generated from the recordings of 10 middle-age (ages 18-28) native Ilokano speakers which were transcribed to phoneme level. The system has a built-in assessment subsystem that evaluates the user's learning based on three categories: reading, listening, and comprehension. The assessment uses a Reading Miscue Detector (RMD) that basically employs force alignment method. An HMM- based Automated Speech Recognition (ASR) system was developed using the phoneme-level transcribed speech corpus as training data and the Viterbi Alignment as the main method for likelihood scoring. The 3-state HMMs were generated using the Hidden Markov Model Toolkit (HTK). A six-week pilot study was conducted to measure the extent of the users' learning of the language gained from the system. The offline test set contains 3 subsets of full system passages by 3 users wherein each phrase has one phoneme with wrong pronunciation. The test set was used to measure the False Alarm Rate (FAR) and Misdetection Rate (MdR) of the RMD, which have shown fairly low percentages or may be considered as a good RMD according to previous studies presented in the literature. The authors demonstrated that 65.75% of the reading miscues can be detected by the system at a false alarm rate of 25.16% which is good enough for similar systems presented in the literature. Another finding of this study is that the speech rhythm of the Ilokano language is mora-timed.