Paper Info
Title
Digit Recognition in the Náhuatl Language: An Evaluation Using Various Recognition Models
Abstract
The aim of Automatic Speech Recognition (ASR) is to develop techniques and systems that enable a computer to accept speech input. The digit recognition task has been often employed contributing to the ASR. In this work, we used parameters of Lineal Prediction Codes (LPC) and Mel Frequency Cepstrum Coefficients (MFCCs). For selection of the best analysis interval we used a Vector Quantization Model. For recognition, we applied the Continuous Density Hidden Markov Model (CDHHM), which employed a dictionary conformed of eighteen command words that are specific digits from the Náhuatl language. The obtained results were compared using Discrete Hidden Markov Models and Vector Quantization Models. In this experiment, we obtained a performance of 99% accuracy for digit recognition. In our experiments we used three native speakers.
Year
DOI
Venue
2010
10.1109/MICAI.2010.27
MICAI (Special Sessions)
Keywords
Field
DocType
vector quantization model,discrete hidden markov models,digit recognition,lineal prediction codes,continuous density hidden markov,mel frequency cepstrum coefficients,automatic speech recognition,huatl language,specific digit,vector quantization models,digit recognition task,various recognition models,vector quantization,speaker recognition,hidden markov models,mel frequency cepstral coefficient,hidden markov model,native speaker,speech recognition,natural language processing,codes,viterbi algorithm,speech
Mel-frequency cepstrum,Computer science,Speaker recognition,Vector quantization,Artificial intelligence,Digit recognition,Viterbi algorithm,Pattern recognition,Nahuatl language,Vector quantisation,Speech recognition,Hidden Markov model,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Sergio Suárez-Guerra1378.81
José Luis Oropeza Rodríguez256.49
Juan Carlos Flores-Paulin300.34
Luis Sánchez43613.87