Urdu Spoken Digits Recognition Using Classified MFCC and Backpropgation Neural Network

Azam, Sheheryar; Mansoor, Z; Mughal, Maryam; Mohsin, Sajjad

doi:10.1109/cgiv.2007.85

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…A comparison of the categorization algorithm with an English language database was also performed. Many works [12,17,22] considered Urdu language voice data for various applications. Hasnain et al in [17] described the frequency analysis of spoken Urdu numbers from 0 to 9.…”

Section: Related Workmentioning

confidence: 99%

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AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

et al. 2021

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The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical speech recognition system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language of Pakistan and is also widely spoken in many other South Asian countries (e.g., India, Afghanistan). Therefore, we present a comprehensive dataset of spoken Urdu digits ranging from 0 to 9. Our dataset has 25,518 sound samples that are collected from 740 participants. To test the proposed dataset, we apply different existing classification algorithms on the datasets including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and flavors of the EfficientNet. These algorithms serve as a baseline. Furthermore, we propose a convolutional neural network (CNN) for audio digit classification. We conduct the experiment using these networks, and the results show that the proposed CNN is efficient and outperforms the baseline algorithms in terms of classification accuracy.

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Section: Related Workmentioning

confidence: 99%

“…Currently, some work studied ASR for Urdu [13][14][15][16][17][18][19][20]. Additionally, numerous works [12,17,[21][22][23] have been done on spoken digit recognition for Urdu. The abovementioned works have shown significant results.…”

Section: Introductionmentioning

confidence: 99%

AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

et al. 2021

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“…The work in [11] presents a system for isolated digit recognition in Urdu and [12] uses a multilayer perceptron to recognize Urdu digits from a single speaker. Finally [13] presents an analysis of Urdu digits to be used for Urdu digit recognition.…”

Section: Urdu Speech Recognition Researchmentioning

confidence: 99%

Large vocabulary continuous speech recognition for Urdu

Sarfraz

Hussain

Bokhari

et al. 2010

Proceedings of the 8th International Conference on Frontiers of Information Technology

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This paper presents the development of acoustic and language models for robust Urdu speech recognition using the CMU Sphinx Open Source Toolkit for speech recognition. Three models have been developed incrementally, with the addition of speech data of up to two speakers per pass; one model using data from 40 female speakers only, one from 41 male speakers only, and one with both male and female speakers (81 speakers). This paper presents the current recognition results, and discusses approaches for improving these recognition rates.

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“…Malay digits has reported a recognition rate of 80.5% and 90.7% when dynamic time warping and Hidden Markov Model (HMM) techniques were used respectively (Al-Haddad et al, 2008), whereas recognition rates of 98% (Azam et al, 2007), 99.5% (Alotaibi, 2005) and 98.125% to 100% (Dede et al, 2010) were reported in a similar application using neural networks.…”

Section: Automatic Speech Recognitionmentioning

confidence: 99%

Automatic Recognition of Speech-evoked Brainstem Responses to English Vowels

Samimi

Forouzanfar

Dajani

2015

Modelling, Identification and Control / 827: Computational Intelligence

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The objective of this study is to investigate automatic recognition of speech-evoked auditory brainstem responses (speech-evoked ABR) to the five English vowels (/a/, /ae/, /ao (ɔ)/, /i/ and /u/). We used different automatic speech recognition methods to discriminate between the responses to the vowels. The best recognition result was obtained by applying principal component analysis (PCA) on the amplitudes of the first ten harmonic components of the envelope following response (based on spectral components at fundamental frequency and its harmonics) and of the frequency following response (based on spectral components in first formant region) and combining these two feature sets. With this combined feature set used as input to an artificial neural network, a recognition accuracy of 83.8% was achieved. This study could be extended to more complex stimuli to improve assessment of the auditory system for speech communication in hearing impaired individuals, and potentially help in the objective fitting of hearing aids.iii

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Urdu Spoken Digits Recognition Using Classified MFCC and Backpropgation Neural Network

Cited by 16 publications

References 5 publications

AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

Large vocabulary continuous speech recognition for Urdu

Automatic Recognition of Speech-evoked Brainstem Responses to English Vowels

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