Bidirectional deep architecture for Arabic speech recognition

Zerari, Naima; Abdelhamid, Samir; Bouzgou, Hassen; Raymond, Christian

doi:10.1515/comp-2019-0004

Cited by 40 publications

(15 citation statements)

References 28 publications

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“…Zerari et al identified labelled basic noun phrases by considering the internal structural features of the phrases, using the corresponding lexical sequences as rules and then performing clipping of the lexical sequences to obtain rule sets. However, the accuracy of recognition needs to be improved [8]. Cui et al selected relevant samples from many stores and used some conventional methods for feature extraction of spectral and rhyme types for a few selected types [9].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

Du¹,

Duan

2021

Complexity

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This paper combines domestic and international research results to analyze and study the difference between the attribute features of English phrase speech and noise to enhance the short-time energy, which is used to improve the threshold judgment sensitivity; noise addition to the discrepancy data set is used to enhance the recognition robustness. The backpropagation algorithm is improved to constrain the range of weight variation, avoid oscillation phenomenon, and shorten the training time. In the real English phrase sound recognition system, there are problems such as massive training data and low training efficiency caused by the super large-scale model parameters of the convolutional neural network. To address these problems, the NWBP algorithm is based on the oscillation phenomenon that tends to occur when searching for the minimum error value in the late training period of the network parameters, using the K-MEANS algorithm to obtain the seed nodes that approach the minimal error value, and using the boundary value rule to reduce the range of weight change to reduce the oscillation phenomenon so that the network error converges as soon as possible and improve the training efficiency. Through simulation experiments, the NWBP algorithm improves the degree of fitting and convergence speed in the training of complex convolutional neural networks compared with other algorithms, reduces the redundant computation, and shortens the training time to a certain extent, and the algorithm has the advantage of accelerating the convergence of the network compared with simple networks. The word tree constraint and its efficient storage structure are introduced, which improves the storage efficiency of the word tree constraint and the retrieval efficiency in the English phrase recognition search.

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Section: Related Workmentioning

confidence: 99%

[Retracted] English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

Du¹,

Duan

2021

Complexity

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“…Indeed, the performance of cov-MLR with N = 100 is 98.7 ± 0.1%, better than using a mean-based decoder with an echo state network of N = 900 neurons (Alalshekmubarak and Smith, 2013), but obtained with 9 times fewer neurons within the reservoir. Furthermore, the performance is at the same level of that obtained with a much more complex network model, as is the long short-term memory network in (Zerari et al, 2019) (98.77%).…”

Section: Resultsmentioning

confidence: 53%

Covariance-based information processing in reservoir computing systems

Lawrie¹,

Moreno-Bote

Gilson

2021

Preprint

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In biological neuronal networks, information representation and processing are achieved through plasticity learning rules that have been empirically characterized as sensitive to second and higher-order statistics in spike trains. However, most models in both computational neuroscience and machine learning aim to convert diverse statistical properties in inputs into first-order statistics in outputs, like in modern deep learning tools. In the context of classification, such schemes have merit for inputs like static images, but they are not well suited to capture the temporal structure in time series. In contrast, the recently developed covariance perceptron uses second-order statistics by mapping input covariances to output covariances in a consistent fashion. Here, we explore the applicability of covariance-based perceptron readouts in reservoir computing networks to classify synthetic multivariate time series structured at different statistical orders (first and second). We show that the second-order framework outperforms or matches the classical mean paradigm in terms of accuracy. We expose nontrivial relationships between input, reservoir and output dynamics, which suggest an important role for recurrent connectivity in transforming information representations in biologically inspired architectures. Finally, we solve a real automatic speech recognition task for the classification of spoken digits to further demonstrate the potential of covariance-based decoding.

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“…Zerari et al. [35] proposed a system to recognise the isolated Arabic utterances using two ASR applications: a) TV spoken command recognition, b) spoken digit recognition. This system consists of several steps: first, pertinent features were extracted from the natural speech (static and dynamic) MFCC features and the Filter Banks (FB) coefficients.…”

Section: Related Workmentioning

confidence: 99%

Arabic speech recognition using end‐to‐end deep learning

Alsayadi

Abdelhamid

Hegazy

et al. 2021

IET Signal Processing

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Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be integrated with other systems better than Arabic ASR methods without diacritics. In this work, the application of state-of-the-art end-to-end deep learning approaches is investigated to build a robust diacritised Arabic ASR. These approaches are based on the Mel-Frequency Cepstral Coefficients and the log Mel-Scale Filter Bank energies as acoustic features. To the best of our knowledge, end-to-end deep learning approach has not been used in the task of diacritised Arabic automatic speech recognition. To fill this gap, this work presents a new CTC-based ASR, CNN-LSTM, and an attention-based end-to-end approach for improving diacritisedArabic ASR. In addition, a word-based language model is employed to achieve better results. The end-to-end approaches applied in this work are based on state-of-the-art frameworks, namely ESPnet and Espresso. Training and testing of these frameworks are performed based on the Standard Arabic Single Speaker Corpus (SASSC), which contains 7 h of modern standard Arabic speech. Experimental results show that the CNN-LSTM with an attention framework outperforms conventional ASR and the Joint CTC-attention ASR framework in the task of Arabic speech recognition. The CNN-LSTM with an attention framework could achieve a word error rate better than conventional ASR and the Joint CTC-attention ASR by 5.24% and 2.62%, respectively.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Bidirectional deep architecture for Arabic speech recognition

Cited by 40 publications

References 28 publications

[Retracted] English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

[Retracted] English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

Covariance-based information processing in reservoir computing systems

Arabic speech recognition using end‐to‐end deep learning

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