Introduction to Arabic Speech Recognition Using CMUSphinx System

Abstract— In this paper Arabic was investigated from the speech recognition problem point of view.

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Abstract— In this paper Arabic was investigated from the speech recognition problem point of view. We propose a novel approach to build an Arabic Automated Speech Recognition System (ASR). This system is based on the open source CMU Sphinx-4, from the Carnegie Mellon University. CMU Sphinx is a large-vocabulary; speaker-independent, continuous speech recognition system based on discrete Hidden Markov Models (HMMs). We build a model using utilities from the OpenSource CMU Sphinx. We will demonstrate the possible adaptability of this system to Arabic voice recognition. Keywords: Speech recognition, Arabic language, HMMs, CMUSphinx-4, Artificial intelligence. I. INTRODUCTION Automatic Speech Recognition (ASR) is a technology that allows a computer to identify the words that a person speaks into a microphone or telephone. It has a wide area of applications: Command recognition (Voice user interface with the computer), Dictation, Interactive Voice Response, it can be used to learn a foreign language. ASR can help also, handicapped people to interact with society. It is a technology which makes life easier and very promising [1]. View the importance of ASR too many systems are developed, the most popular are: Dragon Naturally Speaking, IBM Via voice, Microsoft SAPI. Open source speech recognition systems are available too, such as HTK [2], ISIP [3], AVCSR [4] and CMU Sphinx-4 [5-7]. We are interested to this last, which is based on Hidden Markov Models (HMMs) [8]. A Hidden Markov Model (HMM) is a statistical model where the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters, from the observable parameters, based on this assumption. The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications. Its extension into foreign languages (English is the standard) represent a real research challenge area. Although Arabic is currently one of the most widely spoken language in the world, there has been relatively little speech recognition research on Arabic compared to the other languages [9-11]. The first works on Arabic ASR has concentrated on developing recognizers for modern standard Arabic (MSA). The most difficult problems in developing highly accurate ASRs for Arabic are the predominance of non diacritized text material, the enormous dialectal variety, and the morphological complexity. D. Vergyri et al. investigate the use of morphology-based language model at different stages in a speech recognition system for conversational Arabic [9]. K. Kirchhoff et al. [10] investigate the recognition of dialectal Arabic and study the discrepancies between dialectal and formal Arabic in the speech recognition point of view. D. Vergyri et al [11] investigate the automatic diacritizing Arabic text for use in acoustic model training for ASR. CMU (Carnegie Mellon University) Sphinx speech recognition system is freely

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