Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition

Nahar, Khalid M.O.; Shquier, Mohammed M. Abu; Al-Khatib, Wasfi G.; Al-Muhtaseb, Husni; Elshafei, Moustafa

doi:10.1007/s10772-016-9337-5

Cited by 22 publications

(9 citation statements)

References 15 publications

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“…It was seen that the journals "IEEE Transaction Speech Audio Processing" and "International Journal of Speech and Technology" given the highest number of papers in the study. [62], [63], [64], [65], [66], [67], [68], [69], [70], [44], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [47], [81], [82], [83], [84], [85], [53], [86], [87], [5] Conferences 33 45.2 [88], [63], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [22], [101], [102], [103], [104],…”

Section: A Rq1 Aimed To Find the Various Type Of Research Papers Usementioning

confidence: 99%

Acoustic Modeling in Speech Recognition: A Systematic Review

Bhatt¹,

Jain²,

Dev³

2020

IJACSA

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The paper presents a systematic review of acoustic modeling (AM) techniques in speech recognition(SR). Acoustic modeling establishes a relationship between acoustic information and language construct in SR. Over the past decades, researchers presented studies addressing specific concerns in AM. However, all previous research works lack a systematic and comprehensive review of acoustic modeling issues. A systematic review is introduced to understand the acoustic modeling issues in speech recognition. This paper provides an extensive and comprehensive inspection of various researches that have been performed since 1984. The extensive investigation and analysis into AM was performed by getting the relevant data from 73 research works chose after the screening process between the years from 1984 to 2020. The systematic review process was divided into different parts to investigate acoustic modeling issues. Main issues in acoustic modeling such as feature extraction techniques, acoustic modeling units, speech corpora, classification methods, different tools used, language issues applied, and evaluation parameters were investigated. This study helps the reader to understand various acoustic modeling issues with comprehensive details. The research outcomes presented in this study depict research trends and shed light on new research topics in AM. The result of this review can be used to build a better speech recognition system by choosing a suitable acoustic modeling construct in SR.

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Section: A Rq1 Aimed To Find the Various Type Of Research Papers Usementioning

confidence: 99%

Acoustic Modeling in Speech Recognition: A Systematic Review

Bhatt¹,

Jain²,

Dev³

2020

IJACSA

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“…However, such approaches require a special corpus of transcribed, recorded, and categorized data. Abdou et al (2012) and Nahar et al (2016) offer a hybrid algorithm for recognition. Mainly the algorithm is a combination between linear vector quantization (LVQ) with a hidden Markov model (HMM).…”

Section: Speech Recognition System (Srs)mentioning

confidence: 99%

“…3. The MFCC is calculated using-Equation 1 and its implementation was carried out based on MFCC Algorithm in (Nahar et al, 2016). (Nahar et al, 2016).…”

Section: Mel-frequency Cepstrum Coefficients (Mfcc)mentioning

confidence: 99%

Handicapped Wheelchair Movements Using Discrete Arabic Command Recognition

Nahar¹

2020

SJKFU

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Automatic Speech Recognition (ASR) is an effective and widespread method used to interpret the human voice into commands. Over the past few decades and due to the great evolution in data science, the processing of speech has been embedded in various new technologies. The benefits of such evolution motivated us to develop a discrete voice system for controlling the wheelchair movements for Arab people with physical impairment (handicapped people). The wheelchair via voice system was able to recognize seven isolated words in the Arabic language. The approach uses the CMU-Sphinx4 ASR for evaluating the effectiveness and the accuracy of the recognition system. A corpus was built, and then the system was trained on 70% of this corpus and tested using the rest of 30%. The experiments reveal that the recognition rate of the proposed approach is 96%. The average level of accuracy of the real experiment and corpus-based testing experiment reached 94%.

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“…LVQ aims at determining section areas of each class of the input data. This algorithm has been widely used in various fields, such as for phoneme [2] and character recognition [3] [4], fingerprint verification [5] [6], object orientation detection [7], intrusion detection [8], classification of woven fabric structure [9], variables ordering [10], driving pattern recognition [11], cognitive skill classification [12], textual document classification [13], time series prediction [14], classification of computer network attacks [15], and for improving hierarchical neural networks [16]. Some initial values in LVQ must be defined before the learning process begins.…”

Section: Introductionmentioning

confidence: 99%

Improving learning vector quantization using data reduction

Semadi¹,

Pulungan²

2019

Int. J. Adv. Intell. Informatics

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Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of data involved in the learning process can be reduced by using data reduction methods. In this paper, we propose a data reduction method that uses geometrical proximity of the data. The basic idea is to drop sets of data that have many similarities and keep one representation for each set. By certain adjustments, the data reduction methods can decrease the amount of data involved in the learning process while still maintain the existing accuracy. The amount of data involved in the learning process can be reduced down to 33.22% for the abalone dataset and 55.02% for the bank marketing dataset, respectively.

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Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition

Cited by 22 publications

References 15 publications

Acoustic Modeling in Speech Recognition: A Systematic Review

Acoustic Modeling in Speech Recognition: A Systematic Review

Handicapped Wheelchair Movements Using Discrete Arabic Command Recognition

Improving learning vector quantization using data reduction

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