Automatic Speech Recognition of Quechua Language Using HMM Toolkit - Zevallos, R.; Cordova, J.; Camacho, L. (Springer, 2020)
In this paper, we present the implementation of an Automatic Speech Recognition system (ASR) for southern Quechua language. The software can recognize both continuous speech and isolated words. The ASR was developed using Hidden Markov Model Toolkit (HTK) and the corpus collected by Siminchikkunarayku. A dictionary provides the system with a mapping of vocabulary words to sequences of phonemes; the audio files were processed to extract the speech feature vectors (MFCC) and then, the acoustic model was trained using the MFCC files until its convergence. The paper also describes a detailed architecture of an ASR system developed using HTK library modules and tools. The ASR was tested using the audios recorded by volunteers obtaining a 12.70% word error rate.
Keywords: Hidden Markov model; Computer science; Speech recognition; Mel-frequency cepstrum; Word error rate; Vocabulary; Artificial intelligence; Natural language processing; Feature (linguistics); Word (group theory); Language model; Software; Feature extraction; Linguistics; Programming language; https://purl.org/pe-repo/ocde/ford#1.02.01