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

Darabkh, Khalid A.; Haddad, Laila; Sweidan, Saadeh Z.; Hawa, Mohammed; Saifan, Ramzi; Alnabelsi, Sharhabeel H.

doi:10.1002/cae.21884

Cited by 35 publications

(10 citation statements)

References 58 publications

(85 reference statements)

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“…The most commonly used acoustic feature in speech recognition is the Mel Frequency Cepstral Coefficients (MFCC) [93][94][95]. MFCC was first proposed in Reference [96], which has since become the standard algorithm for representing speech features.…”

Section: Spoken Digitsmentioning

confidence: 99%

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

2020

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Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a Dynamic Vision Sensor (DVS)/EletroMyoGraphy (EMG) hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system’s topology is not fixed by the user but learned along the system’s experience through self-organization.

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Section: Spoken Digitsmentioning

confidence: 99%

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

2020

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“…The MFFCs only present the static characteristics of the intended speech word. However, the human auditory system sensitivity is higher for the dynamic characteristics of sound [24,26]. The intended word dynamic features can be obtained by derivation of MFCCs.…”

Section: A/d Conversionmentioning

confidence: 99%

“…It has diverse applications like smart cities, secure access, rehabilitation centres, criminals investigation analysis, banks, hands free assistance, commanding a robot, data security, virtual reality, Multimedia searches, auto-attendants, travel Information and reservation, translators, natural language understanding, etc. [15,16,[19][20][21][22][23][24]. These systems are founded on the machine based speech comprehension and recognition.…”

Section: Introductionmentioning

confidence: 99%

Speech Recognition and a Cymatics Based Configurable Speech Perception

Qaisar¹

2018

Preprint

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This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

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“…However, the speech and audio encoding methods have not taken into consideration the combination of psychoacoustic effects, computational efficiency, and pattern classification performance for neuromorphic implementation. In the SNN applications for speech recognition (Xiao et al, 2016;Darabkh et al, 2018), MFCC (Mermelstein, 1976) are commonly used as the spectral representation in speech recognition. Others have tried to use the biologically plausible cochlear filter bank, but they are either analog filters which are prone to changes in the external environment (Liu and Delbruck, 2010), or yet to be studied in a spike-driven SNN system (Loiselle et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks

Pan

Chua

et al. 2020

Front. Neurosci.

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The auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audio, into an efficient, effective and reconstructable spike pattern to facilitate the subsequent processing. However, most of the auditory front-ends in current studies have not made use of recent findings in psychoacoustics and physiology concerning human listening. In this paper, we propose a neural encoding and decoding scheme that is optimized for audio processing. The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve. We evaluate the perceptual quality of the BAE scheme using PESQ; the performance of the BAE based on sound classification and speech recognition experiments. Finally, we also built and published two spikeversion of speech datasets: the Spike-TIDIGITS and the Spike-TIMIT, for researchers to use and benchmarking of future SNN research.

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

Cited by 35 publications

References 58 publications

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

Speech Recognition and a Cymatics Based Configurable Speech Perception

An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks

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