Digit Recognition in the Náhuatl Language: An Evaluation Using Various Recognition Models

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.