CASRA+: A Colloquial Arabic Speech Recognition Application

Haraty, Ramzi A.; Ariss, Omar El

doi:10.3844/ajassp.2007.23.32

Cited by 18 publications

(3 citation statements)

References 19 publications

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“…The rate at which zero crossings occur is a simple measure of the frequency content of a signal. Zero crossing rate is therefore a measure of number of times in a given time interval that the amplitude of the signals passes through a value of zero (Haraty and Ariss, 2007).…”

Section: Long Term Spectrummentioning

confidence: 99%

Vibration Analysis of Gasoline Engine Faults

Chomphan¹

2013

American Journal of Applied Sciences

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Vibration analysis of engine faults is an approach to diagnose the engine condition. This study presents a study of vibration analysis of the normal engine and the engine with three different fault conditions. The gasoline engine was selected in this study. The accelerometer has been used at the surface of the engine to measure the vibration in the form of acceleration for all possible directions. Three conditions of engine faults including the engine that is not smooth while idling, the engine that goes missing while idling and the engine that has no power are selected. Five vibration signal parameters including fundamental frequency, long term spectrum, energy, long term cepstrum and zero crossing rate, are computed from all databases. The significant differences between normal engine and the fault engines are concluded. It can be obviously seen that the signal parameters are able to discriminate all three conditions and the engine with normal condition

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Section: Long Term Spectrummentioning

confidence: 99%

Vibration Analysis of Gasoline Engine Faults

Chomphan¹

2013

American Journal of Applied Sciences

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“…By converting the acoustic signal obtained from a microphone or a telephone the speech recognition process generates a set of words (Singh et al 2010;Othman and Riadh 2008). In order to extract and determine the linguistic information conveyed by a speech wave we have to employ computers or electronic circuits (Haraty and El Ariss 2007). This process is utilized for several applications like security device, household appliances, cellular phones, automated teller machines (ATM) and computers (Patel and Rao 2010) Gender classification is applied in many fields.…”

Section: Introductionmentioning

confidence: 99%

Classification of speech signal based on gender: a hybrid approach using neuro-fuzzy systems

Gomathy

Meena²,

Subramaniam

2011

Int J Speech Technol

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One of the most important processes in speech processing is gender classification. Generally gender classification is done by considering pitch as feature. In general the pitch value of female is higher than the male. In some cases, pitch value of male is higher and female is low, in that cases this classification will not obtain the exact result. By considering this drawback here proposed a gender classification method which considers three features and uses fuzzy logic and neural network to identify the given speech signal belongs to which gender. For training fuzzy logic and neural network, training dataset is generated by considering the above three features. After completion of training, a speech signal is given as input, fuzzy and neural network gives an output, for that output mean value is taken and this value gives the speech signal belongs to which gender. The result shows the performance of our method in gender classification.

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“…Perceptual-based evaluation of human raters is not only to simply value non-native utterances as accepted/rejected but also to analyze and locate specific errors on segmental aspects. Further, the acoustic model adaptation is combined with three speaker adaptation techniques Maximum Likelihood Linear Regression (MLLR) as proposed in (Goronzy et al, 2004;Giuliani et al, 2006;Haraty and El Ariss, 2007), Constrained MLLR (CMLLR) and Vocal Track Length ormalization (VTLN) as proposed in (Hariharan et al, 2002;Sundermann et al, 2003;Legetter and Woodland, 1995;Shen and Reynolds, 2008;Al-Haddad et al, 2009;Gales and Young, 2008) in order to eliminate interspeaker variability. Performance of the proposed acoustic model adaptation is evaluated in five measures of alignment analysis between recognition results and perceptual based evaluation: Hit, False Alarm (FA), Miss, Rejection and Hit + Rejection.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Model Adaptation for Indonesian Language Utterance Training System

Linda¹,

Chisaki

Usagawa

2010

Journal of Computer Science

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Problem statement: In order to build an utterance training system for Indonesian language, a speech recognition system designed for Indonesian is necessary. However, the system hardly works well due to the pronunciation variants of non-native utterances may lead to substitution/deletion error. This research investigated the pronunciation variant and proposes acoustic model adaptation to improve performance of the system. Approach: The proposed acoustic model adaptation worked in three steps: to analyze pronunciation variant with knowledge-based and data-derived methods; to align knowledge-based and data-derived results in order to list frequently mispronounced phones with their variants; to perform a state-clustering procedure with the list obtained from the second step. Further, three Speaker Adaptation (SA) techniques were used in combination with the acoustic model adaptation and they are compared each other. In order to evaluate and tune the adaptation techniques, perceptual-based evaluation by three human raters is performed to obtain the "true"recognition results. Results: The proposed method achieved an average gain in Hit + Rejection (the percentage of correctly accepted and correctly rejected utterances by the system as the human raters do) of 2.9 points and 2 points for native and non-native subjects, respectively, when compared with the system without adaptation. Average gains of 12.7 and 6.2 points for native and non-native students in Hit + Rejection were obtained by combining SA to the acoustic model adaptation. Conclusion/Recommendations: Performance evaluation of the adapted system demonstrated that the proposed acoustic model adaptation can improve Hit even though there is a slight increase of False Alarm (FA, the percentage of incorrectly accepted utterances by the system of which the human raters reject). The performance of the proposed acoustic model adaptation depends strongly on the effectiveness of state-clustering procedure to recover only in-vocabulary words. For future research, a confidence measure to discriminate between in-vocabulary and out-vocabulary words will be investigated

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CASRA+: A Colloquial Arabic Speech Recognition Application

Cited by 18 publications

References 19 publications

Vibration Analysis of Gasoline Engine Faults

Vibration Analysis of Gasoline Engine Faults

Classification of speech signal based on gender: a hybrid approach using neuro-fuzzy systems

Acoustic Model Adaptation for Indonesian Language Utterance Training System

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