Abstract
There has been a considerable stream in ASR over the past few decades, but it may seem strange why this field is still a subject for researchers to work on. There are many reasons, but somewhat because the discipline is created with the promise of human-level performance under pragmatic states and this is an inextricable problem. In addition, the increasing advancement of technology in various fields has caused a more compelling need for this field. Especially the establishment of such a system in the security sector in insecure third world countries such as Afghanistan is an urgent need. This paper began with the reflection of all the necessary knowledge about speech recognition and then suggested an unprecedented method for building an automated speech recognition (ASR) system in the Dari language using the two most powerful open source engines CMUSphinx, from Carnegie Mellon University and DeepSpeech v0.9.3 /. These systems are much more impressive than early speech recognition systems. Using my own collected dataset, a speech-to-text model has been trained for the Dari language. Firstly, the dataset is filtered according to the task, then demonstrated the possible compatibility from the hidden Markov (HMM) models, the phoneme concept to RNN training. The system surpassed previously predicted results, as CMUSphinx stated, “for a typical 10-hour operation, the WER should be around 10%. Finally, 3.3% WER was achieved with 10.3-hours of audio recording using CMUSphinx. 1% WER with DeepSpeech.
Highlights
From prehistoric times until now, the exchange of information and considerable effort in interlocution has been and will be a considerable purpose to improve human understanding, so that hearing twiddles an important role in this process
Automatic Speech Recognition (ASR) is a technology that allows a computer to identify the words that a person speaks into a microphone or telephone [2]
I got a subset of my own collected dataset and start training with CMUSphinx with the following configuration: CFG_HMM_TYPE='.cont; $CFG_FEATURE="s2_4x"; $CFG_NUM_STREAMS=4; $CFG_INITIAL_NUM_DENSITIES=256; $CFG_FINAL_NUM_DENSITIES=256; $CFG_N_TIED_STATES=2000; $CFG_MMIE="yes"; $CFG_G2P_MODEL='yes'; $DEC_CFG_VERBOSE=1 After training, I got two different folders with different files under the name of model architecture and model parameter following WER of 3.3% and noticed an increase in performance and got high accuracy, CMUSphinx is the best approach for speech recognition because with a fewer number of the dataset you can create a good model, probabilistic works well as the problems with creating Speech-to-text model are the altered speaking manner, homophone, homograph and distorted acoustic like pray/prey
Summary
From prehistoric times until now, the exchange of information and considerable effort in interlocution has been and will be a considerable purpose to improve human understanding, so that hearing twiddles an important role in this process. Hearing hinges on a series of complex and intricate stages which convert sound waves in the air into electrical signals. The brain receives these signals with the help of the auditory nerve. In the continuation of this article, the concept of a speech recognition system will be discussed It begins by describing the basic complexity of the neural network and the process of training your own dataset with hard encryption, starting from scratch, followed by a discussion on CMU Sphinx and how to prepare your own dataset (Section 3). Last it concludes with experimental results (Section 5), followed by conclusions
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