Hidden Markov model and Persian speech recognition

Nowadays, speech recognition, which simply refers to the process of converting an audio signal into its equivalent text, has become one of the most important research topics. Although many studies have been conducted in the field of speech recognition for many languages of the world, but can be said that no more study has been conducted in the Persian language and therefore it is necessary to conduct more studies in this field. Since Persian is a rich language that can create many new words by adding a suffix (prefix) to its main root, so it can be said that the success rate of voice recognition programs in this language has also increased with the increase in the number of phonemes and therefore can have a significant improvement. Therefore, in this study, a practical approach to Persian speech recognition based on syllables, which are a unit between phonemes and words, has been used and done by the hidden Markov model. After obtaining syllable utterances, multiple coefficients are calculated for all syllables. Finally, suitable models were created and the success rate was calculated by conducting tests for the systems. To measure the performance of the system, the error rate criterion was used. The results of this study show that the word error rate for the hidden Markov model was 18.3% and increased the system performance by approximately 16% after post-processing.