Novel approaches to Arabic speech recognition: report from the 2002 Johns-Hopkins Summer Workshop

Kirchhoff, Katrin; Bilmes, Jeffrey A.; Das, Surajeet; Duta, Nicolae; Egan, Melissa; Ji, Gang; He, Feng; Henderson, John C.; Liu, Daben; Noamany, Mohamed; Schone, Patrick; Schwartz, Richard; Vergyri, Dimitra

doi:10.1109/icassp.2003.1198788

Cited by 78 publications

(68 citation statements)

References 4 publications

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“…Gal (2002) Kirchoff et al (2002) focuses on vocalizing transcripts of oral conversational Arabic. Since conversational Arabic is much more free-flowing, and prone to dialect and speaker differences, diacritization of such transcripts proves much more difficult.…”

Section: Related Workmentioning

confidence: 99%

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Arabic diacritization using weighted finite-state transducers

Nelken

Shieber

2005

Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages - Semitic '05

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Arabic is usually written without short vowels and additional diacritics, which are nevertheless important for several applications. We present a novel algorithm for restoring these symbols, using a cascade of probabilistic finitestate transducers trained on the Arabic treebank, integrating a word-based language model, a letter-based language model, and an extremely simple morphological model. This combination of probabilistic methods and simple linguistic information yields high levels of accuracy.

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Section: Related Workmentioning

confidence: 99%

“…Diacritization is even more problematic for computational systems, adding another level of ambiguity to both analysis and generation of text. For example, full vocalization is required for textto-speech applications, and has been shown to improve speech-recognition perplexity and error rate (Kirchoff et al, 2002). …”

Section: Introductionmentioning

confidence: 99%

Arabic diacritization using weighted finite-state transducers

Nelken

Shieber

2005

Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages - Semitic '05

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“…The system focused on recognizing the Egyptian dialect in addition to the English and Spanish language. Recent works that based there testing on the same ECA CallHome corpus were researched by [1,2,12] . Papers [1,2] focused on the improvement of the morphological aspect of the language model, while [12] focused on the cross-dialectical data using standard Arabic to improve the recognition rate of ECA.…”

Section: Variability Caused By Dialectical Differencesmentioning

confidence: 99%

“…This high growth rate is problematic for language models by causing a large number of out-of-vocabulary words. Papers [1,2] address the effect of morphology on Arabic language speech recognition systems.…”

Section: Variability Caused By Dialectical Differencesmentioning

confidence: 99%

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CASRA+: A Colloquial Arabic Speech Recognition Application

Haraty¹,

Ariss²

2007

American J. of Applied Sciences

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The research proposed here was for an Arabic speech recognition application, concentrating on the Lebanese dialect. The system starts by sampling the speech, which was the process of transforming the sound from analog to digital and then extracts the features by using the Mel-Frequency Cepstral Coefficients (MFCC). The extracted features are then compared with the system's stored model; in this case the stored model chosen was a phoneme-based model. This reference model differs from the direct word template matching, where speech features that are extracted from the input are directly compared to the word templates. Each word template in the direct matching model was stored as a vector of feature parameters. Thus, when the vocabulary size of the ASR system becomes large, the memory size for the word template will become humongous. In contrast, the model used here was phoneme-like template matching. Word templates are stored as phoneme-like template parameters. Thus, the memory size for the word templates will not grow as fast as that of the direct matching model.

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An efficient speech recognition system for arm‐disabled students based on isolated words

Darabkh

Haddad

Sweidan

et al. 2017

Comp Applic In Engineering

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Over the previous decades, a need has emerged to empower human‐machine communication systems, which are essential to not only perform actions, but also obtain information especially in education applications. Moreover, any communication system has to introduce an efficient and easy way for interaction with a minimum possible error rate. The keyboard, mouse, trackball, touch‐screen, and joystick are all examples of tools which were built to provide mechanical human‐to‐machine interaction. However, a system with the ability to use oral speech, which is the natural form of communication between humans instead of mechanical communication systems, can be more practical for normal students and even a necessity for arm‐disabled students who cannot use their arms to handle traditional education tools like pens and notebooks. In this paper, we present a speech recognition system that allows arm‐disabled students to control computers by voice as a helping tool in the educational process. When a student speaks through a microphone, the speech is divided into isolated words which are compared with a predefined database of huge number of spoken words to find a match. After that, each recognized word is translated into its related tasks which will be performed by the computer like opening a teaching application or renaming a file. The speech recognition process discussed in this paper involves two separate approaches; the first approach is based on double thresholds voice activity detection and improved Mel‐frequency cepstral coefficients (MFCC), while the second approach is based on discrete wavelet transform along with modified MFCC algorithm. Utilizing the best values for all parameters in just mentioned techniques, our proposed system achieved a recognition rate of 98.7% using the first approach, and 98.86% using the second approach of which is better in ratio than the first one but slower in processing which is a critical point for a real time system. Both proposed approaches were compared with other relevant approaches and their recognition rates were noticeably higher.

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Novel approaches to Arabic speech recognition: report from the 2002 Johns-Hopkins Summer Workshop

Cited by 78 publications

References 4 publications

Arabic diacritization using weighted finite-state transducers

Arabic diacritization using weighted finite-state transducers

CASRA+: A Colloquial Arabic Speech Recognition Application

An efficient speech recognition system for arm‐disabled students based on isolated words

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