Generation of Arabic phonetic dictionaries for speech recognition

Interspeech 2009, 2009

In this paper, we describe the use of either words or morphemes as lexical modeling units and the use of either graphemes or phonemes as phonetic modeling units for Arabic automatic speech recognition (ASR). We designed four Arabic ASR systems: two word-based systems and two morpheme-based systems. Experimental results using these four systems show that they have comparable state-of-the-art performance individually, but the more sophisticated morpheme-based system tends to be the best. However, they seem to complement each other quite well within the ROVER system combination framework to produce substantially-improved combined results.

2020

The phonetic dictionary, which is also known as pronunciation dictionary and lexicon, contains a mapping between the words of the intended language and their corresponding phoneme sequences. It is an essential component for developing an Automatic Speech Recognition (ASR) system. The phonetic dictionary acts as an intermediary component with other ASR components such as the acoustic model and the language model. This research aims to design and develop a phonetic dictionary generator (PDG) for Modern Standard Arabic (MSA) and Arabic disordered speech that can be used for ASR research and development. This rule-based PDG receives Arabic texts and transforms them into their corresponding phoneme sequences using pre-defined rules. Since ASR systems must accommodate different needs of speakers and users including the speech of normal speakers and speakers with speech and articulation disorders, this PDG will be able to produce phoneme sequences for both normal speech and disordered spee...

The Automatic Speech Recognition (ASR) applied to Arabic language is a challenging task. This is mainly related to the language specificities which make the researchers facing multiple difficulties such as the insufficient linguistic resources and the very limited number of available transcribed Arabic speech corpora. In this paper, we are interested in the development of a HMM-based ASR system for Standard Arabic (SA) language. Our fundamental research goal is to select the most appropriate acoustic parameters describing each audio frame, acoustic models and speech recognition unit. To achieve this purpose, we analyze the effect of varying frame windowing (size and period), acoustic parameter number resulting from features extraction methods traditionally used in ASR, speech recognition unit, Gaussian number per HMM state and number of embedded re-estimations of the Baum-Welch Algorithm. To evaluate the proposed ASR system, a multi-speaker SA connected-digits corpus is collected, transcribed and used throughout all experiments. A further evaluation is conducted on a speaker-independent continue SA speech corpus. The phonemes recognition rate is 94.02% which is relatively high when comparing it with another ASR system evaluated on the same corpus.

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008

In this paper two aspects of generating and using phonetic Arabic dictionaries are described. First, the use of single pronunciation acoustic models in the context of Arabic large vocabulary Automatic Speech Recognition (ASR) is investigated. These have been found to be useful for English ASR systems, when combined with standard multiple pronunciation systems. The second area examined is automatically deriving phonetic "pronunciations" for words that standard approaches, such as the Buckwalter Morphological Analyzer, cannot handle. Without pronunciations for these words the OOV rates for various Arabic tasks significantly increase. Here, pronunciations are automatically found by first deriving grapheme-to-phone rules, and associated rule probabilities. These are then used to produce the most likely pronunciation, or pronunciations, for any word. These approaches are evaluated on a large vocabulary Arabic Broadcast News and Broadcast Conversation transcription task. Both schemes are found to yield gains with a multi-pass/combination framework.

International Conference on Communications, Control and Signal Processing, 2011

The Automatic Speech Recognition (ASR) applied to Arabic language is a challenging task. This is mainly related to the language specificities which make the researchers facing multiple difficulties such as the insufficient linguistic resources and the very limited number of available transcribed Arabic speech corpora. In this paper, we are interested in the development of a HMM-based ASR system for Standard Arabic (SA) language. Our fundamental research goal is to select the most appropriate acoustic parameters describing each audio frame, acoustic models and speech recognition unit. To achieve this purpose, we analyze the effect of varying frame windowing (size and period), acoustic parameter number resulting from features extraction methods traditionally used in ASR, speech recognition unit, Gaussian number per HMM state and number of embedded re-estimations of the Baum-Welch Algorithm. To evaluate the proposed ASR system, a multi-speaker SA connected-digits corpus is collected, transcribed and used throughout all experiments. A further evaluation is conducted on a speaker-independent continue SA speech corpus. The phonemes recognition rate is 94.02% which is relatively high when comparing it with another ASR system evaluated on the same corpus.

2013

This paper describes the use of an incremental methodology for improving Arabic pronunciation dictionaries (PDs). We will show how the incremental cycles can be applied to Arabic speech recognition to reduce Word Error Rates (WER). By using phonology and morphology rules, the incremental methodology is used to improve the PDs of Modern Standard Arabic (MSA) and the Levantine dialect. The WER is improved in absolute accuracy of 5.3% for MSA PD and 5% for Levantine dialect. Keywords; An incremental methodology; MSA; Levantine dialect; PDs; WER

Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right hamoza above or below Alif.

Proceedings of the 7th Wseas International Conference on Signal Processing Robotics and Automation, 2008

The performance of well-trained speech recognizers using high quality full bandwidth speech data is usually degraded when used in real world environments. In particular, telephone speech recognition is extremely difficult due to the limited bandwidth of transmission channels. In this paper, we concentrate on the telephone recognition of Egyptian Arabic speech using syllables. Arabic spoken digits were described by showing their constructing phonemes, triphones, syllables and words. Speaker-independent hidden markov models (HMMs)-based speech recognition system was designed using Hidden markov model toolkit (HTK). The database used for both training and testing consists from forty-four Egyptian speakers. In clean environment, experiments show that the recognition rate using syllables outperformed the rate obtained using monophones, triphones and words by 2.68%, 1.19% and 1.79% respectively. Also in noisy telephone channel, syllables outperformed the rate obtained using monophones, triphones and words by 2.09%, 1.5% and 0.9% respectively. Comparative experiments have indicated that the use of syllables as acoustic units leads to an improvement in the recognition performance of HMM-based ASR systems in noisy environments. A syllable unit spans a longer time frame, typically three phones, thereby offering a more parsimonious framework for modeling pronunciation variation in spontaneous speech. Moreover, syllable-based recognition has relatively smaller number of used units and runs faster than word-based recognition.

Lecture Notes in Computer Science, 2010

Automatic recognition of spoken alphabets is one of the difficult tasks in the field of computer speech recognition. In this research, spoken Arabic alphabets are investigated from the speech recognition problem point of view. The system is designed to recognize an isolated whole-word speech. The Hidden Markov Model Toolkit (HTK) is used to implement the isolated word recognizer with phoneme based HMM models. In the training and testing phase of this system, isolated alphabets data sets are taken from the telephony Arabic speech corpus, SAAVB. This standard corpus was developed by KACST and it is classified as a noisy speech database. A hidden Markov model based speech recognition system was designed and tested with automatic Arabic alphabets recognition. Four different experiments were conducted on these subsets, the first three trained and tested by using each individual subset, the fourth one conducted on these three subsets collectively. The recognition system achieved 64.06% overall correct alphabets recognition using mixed training and testing subsets collectively.

IEEE access, 2024

Speech recognition is a captivating process that revolutionizes human-computer interactions, allowing us to interact and control machines through spoken commands. The foundation of speech recognition lies in understanding a given language's linguistic and textual characteristics. Although automatic speech recognition (ASR) systems flawlessly convert speech into text for various international languages, their implementation for Arabic remains inadequate. In this research, we diligently explore the current state of Arabic ASR systems and unveil the challenges encountered during their development. We categorize these challenges into two groups: those specific to the Arabic language and those that are more general. Additionally, we propose strategies to overcome these obstacles and emphasize the need for ASR architectures tailored to the Arabic language's unique grammatical and phonetic structure. In addition, we provide a comprehensive and explicit description of various feature extraction methods, language models, and acoustic models utilized in the Arabic ASR system.