An experimental framework for Arabic digits speech recognition in noisy environments

Touazi, Azzedine; Debyeche, Mohamed

doi:10.1007/s10772-017-9400-x

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…In terms of studying the robustness of speech recognition systems for the Arabic language, some research has been done in this field. For example, Touazi and Debyeche [15] presented ARADIGIT-2, a database for Arabic digit recognition based on the Hidden Markov Models (HTK) toolkit and independent Arabic speakers. They used it to evaluate systems' robustness to noise under different conditions and types of noise.…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

OUISAADANE,

SAFI,

FRIKEL

2023

Preprint

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In this study, we introduce an experimental framework for Moroccan Dialect speech recognition under various additive noise conditions using the open-source tool PocketSphinx. We curated a corpus comprising the ten most commonly used greetings in the Moroccan dialect, extracted from telephone conversations. This corpus was recorded with 60 speakers (30 males and 30 females). Each speaker articulated each expression three times in natural and noisy conditions. Feature extraction utilized Mel Scale Cepstral Coefficients (MFCC), and acoustic modeling, based on monophony, was implemented using Hidden Markov Models (HMM). While automatic speech recognition systems demonstrate commendable performance in noise-free conditions, their efficacy noticeably diminishes in the presence of noise.

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Section: Related Workmentioning

confidence: 99%

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

OUISAADANE,

SAFI,

FRIKEL

2023

Preprint

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“…The experiments were carried out on Arabic Digits database (ARADIGITS-2) consists of 2704 clean utterances by 112 speakers. The syllable level acoustic units outperform word level units by an average word accuracy rate of 0.44 and 0.58% for clean condition and multi condition training tasks respectively [5].…”

Section: Literature Surveymentioning

confidence: 97%

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

2019

ijrte

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A digit recognition system is built for recognizing the sequence of digits through 0-9. The system is experimented with speech corpus created in the room environment. The acoustic information to feature representation is achieved using PLP and MFCC features. The system initially utilized the conventional GMM-HMM framework, state of the art hybrid classifier with varied number of states to complete the speech recognition task, i.e., the system is first trained and tested using Monophone models, and system’s recognition accuracy is then evaluated using Triphone Models: Triphone1 models, which was later followed by Triphones2 models and Triphones3 Models. The Ngram Language model is used for both Monophone and Triphone training. The system performance is evaluated with the use of MFCC and PLP parameterisation techniques on Kaldi toolkit. The system performance is evaluated using metrics word error rate (WER) and Word Recognition Accuracy (WRA). The proposed system can be utilized for building speech applications

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“…The Arabic language is the fourth largest language spoken by nearly 1.6 billion Muslims native speakers, this language spoken by the majority of the people in the Middle East and North Africa; note that Arabic has many different dialects. This is some little work in Arabic speaker and speech recognition [35][36][37][38]. We presented in Table 1 a Literature review for speech recognition research using MFCC, GMM and VQ techniques regarding Arabic or other languages.…”

Section: Introductionmentioning

confidence: 99%

Arabic digits speech recognition and speaker identification in noisy environment using a hybrid model of VQ and GMM

2020

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This paper presents an automatic speaker identification and speech recognition for Arabic digits in noisy environment. In this work, the proposed system is able to identify the speaker after saving his voice in the database and adding noise. The mel frequency cepstral coefficients (MFCC) is the best approach used in building a program in the Matlab platform; also, the quantization is used for generating the codebooks. The Gaussian mixture modelling (GMM) algorithms are used to generate template, feature-matching purpose. In this paper, we have proposed a system based on MFCC-GMM and MFCC-VQ approaches on the one hand and by using the hybrid approach MFCC-VQ-GMM on the other hand for speaker modeling. The white Gaussian noise is added to the clean speech at several signal-to-noise ratio (SNR) levels to test the system in a noisy environment. The proposed system gives good results in recognition rate.

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An experimental framework for Arabic digits speech recognition in noisy environments

Cited by 8 publications

References 26 publications

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

WITHDRAWN: An Experiment of Moroccan Dialect speech recognition in noisy environments using PocketSphinx

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

Arabic digits speech recognition and speaker identification in noisy environment using a hybrid model of VQ and GMM

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