41 research outputs found

    QVR: Quranic Verses Recitation Recognition System Using PocketSphinx

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    The recitation of Quran verses according to the actual tajweed is obligatory and it must be accurate and precise in pronunciation. Hence, it should always be reviewed by an expert on the recitation of the Quran. Through the latest technology, this recitation review can be implemented through an application system and it is most appropriate in this current Covid-19 pandemic situation where system application online is deemed to be developed. In this empirical study, a recognition system using PocketSphinx to convert the Quranic verse from sound to text, and determine the accuracy of reciters has been developed so-called the Quranic Verse Recitation Recognition (QVR) system. The Graphical User Interface (GUI) of the system with a user-friendly environment was designed using Microsoft Visual Basic 6 in an Ubuntu platform. A verse of surah al-Ikhlas has been chosen in this study and the data were collected by recording 855 audios as training data recorded by professional reciters. Another 105 audios were collected as testing data, to test the accuracy of the system. The results indicate that the system obtained a 100% accuracy with a 0.00% of word error rate (WER) for both training and testing data of the said audios. The system with automatic speech recognition (ASR) engine system demonstrates that it has been successfully designed and developed, and is significant to be extended further. Added, it will be improved with the addition of other Quran surahs

    Towards using CMU Sphinx Tools for the Holy Quran recitation verification

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    The use of the Automatic Speech Recognition (ASR) technology is being used is many different applications that help simplify the interaction with a wider range of devices. This paper investigates the use of a simplified set of phonemes in an ASR system applied to Holy Quran. The Carnegie Mellon University Sphinx 4 tools were used to train and evaluate an acoustic model on Holy Quran recitations that are widely available online. The building of the acoustic model was done using a simplified list of phonemes instead of the mainly used Romanized in order to simplify the process of training the acoustic model. In this paper, the experiment resulted in Word Error Rates (WER) as low as 1.5% even with a very small set of audio files to use in the training phase

    Continuous Density Hidden Markov Model for Hindi Speech Recognition

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    State of the art automatic speech recognitionsystem uses Mel frequency cepstral coefficients as featureextractor along with Gaussian mixture model for acousticmodeling but there is no standard value to assign number ofmixture component in speech recognition process.Currentchoice of mixture component is arbitrary with littlejustification. Also the standard set for European languagescan not be used in Hindi speech recognition due to mismatchin database size of the languages.Parameter estimation withtoo many or few component may inappropriately estimatethe mixture model. Therefore, number of mixture isimportant for initial estimation of expectation maximizationprocess. In this research work, the authors estimate numberof Gaussian mixture component for Hindi database basedupon the size of vocabulary.Mel frequency cepstral featureand perceptual linear predictive feature along with itsextended variations with delta-delta-delta feature have beenused to evaluate this number based on optimal recognitionscore of the system . Comparitive analysis of recognitionperformance for both the feature extraction methods onmedium size Hindi database is also presented in thispaper.HLDA has been used as feature reduction techniqueand also its impact on the recognition score has beenhighlighted

    Inequity in Popular Voice Recognition Systems Regarding African Accents

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    With new age speakers such as the Echo Dot and Google Home, everyone should have equal opportunity to use them. Yet, for many popular voice recognition systems, the only accents that have wide support are those from Europe, Latin America, and Asia. This can be frustrating for users who have dialects or accents which are poorly understood by common tools like Amazon's Alexa. As such devices become more like household appliances, researchers are becoming increasingly aware of bias and inequity in Speech Recognition, as well as other sub-fields of Artificial Intelligence. The addition of African accents can potentially diversify smart speaker customer bases worldwide. My research project can help developers include accents from the African diaspora as they build these systems. In this work, we measure recognition accuracy for under-represented dialects across a variety of speech recognition systems and analyze the results in terms of standard performance metrics. After collecting audio files from different voices across the African diaspora, we discuss key findings and generate guidelines for developing an implementation for current voice recognition systems that are more fair for all
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