پایه‌گذاری بستری نو و کارآمد در حوزه بازشناسی گفتار فارسی

Although researches in the field of Persian speech recognition  claim  a  thirty-year-old  history in Iran  which has achieved considerable progresses, due to the lack of well-defined experimental framework, outcomes from many of these researches are not comparable to each other and their accurate assessment won’t be possible. The experimental framework includes ASR toolkit and speech database which consists of training, development and test datasets. In recent years,   as a state-of-the-art open-source ASR toolkit; Kaldi has been very well-received and welcomed in the community of the world-ranked speech researchers around the world. considering all aspects, Kaldi is the best option among all of the other ASR toolkits to establish a framework to do research in all languages, including Persian. In this paper, we chose Fardat as the speech database which is the counterpart of TIMIT for Persian language because not only it has got a standard form  but it’s also accessible for all researchers around the world. Similar to the recipe on TIMIT database, we defined these three sets on the Farsdat: Training, Development and Test sets. After a survey on Kaldi’s components and features, we applied most of state-of-the-art ASR techniques in the Kaldi on the Farsdat based on three sets definition. The best phone error rate on development and test set have been 20.3% and 19.8%. All of the codes and the recipe that was written by author have been submitted to Kaldi repository and they are accessible  for free, so all the reported results  will be easily replicable if you have access to Farsdat database.