A Pashtu speakers database using accent and dialect approach

Shah, id Munir; Memon, Saima Q.; Moinuddin, Muhammad

doi:10.1504/ijapr.2017.089398

Cited by 2 publications

(3 citation statements)

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“…ere are three major dialectical variations in Pashto, i.e., Southern, Northern, and Central [60]. Northern and Central dialectical variations are mostly spoken in Pakistan, whereas the Southern dialectical variations are mostly spoken in Afghanistan [61]. Each of these major dialectical variations is further divided into various subdialects [62].…”

Section: Dialectical Variations In Pashtomentioning

confidence: 99%

“…In the designed script, 25 separate daily conversational-based short-duration (1 to 3 seconds) sentences were included. In particular, those words were included in the sentences, which are pronounced or spoken in a different way in the selected dialects (refer [61] for detail on the written script and the included words in the script).…”

Section: Afghanistan Pakistan Border Main Citiesmentioning

confidence: 99%

“…e data set used in this research is the extension of the data set presented in [61]. Complete data set has been collected with self-efforts.…”

Section: Data Availabilitymentioning

confidence: 99%

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A Robust Approach for Speaker Identification Using Dialect Information

Shah

Moinuddin²,

Khan

2022

Applied Computational Intelligence and Soft Computing

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The present research is an effort to enhance the performance of voice processing systems, in our case the speaker identification system (SIS) by addressing the variability caused by the dialectical variations of a language. We present an effective solution to reduce dialect-related variability from voice processing systems. The proposed method minimizes the system’s complexity by reducing search space during the testing process of speaker identification. The speaker is searched from the set of speakers of the identified dialect instead of all the speakers present in system training. The study is conducted on the Pashto language, and the voice data samples are collected from native Pashto speakers of specific regions of Pakistan and Afghanistan where Pashto is spoken with different dialectal variations. The task of speaker identification is achieved with the help of a novel hierarchical framework that works in two steps. In the first step, the speaker’s dialect is identified. For automated dialect identification, the spectral and prosodic features have been used in conjunction with Gaussian mixture model (GMM). In the second step, the speaker is identified using a multilayer perceptron (MLP)-based speaker identification system, which gets aggregated input from the first step, i.e., dialect identification along with prosodic and spectral features. The robustness of the proposed SIS is compared with traditional state-of-the-art methods in the literature. The results show that the proposed framework is better in terms of average speaker recognition accuracy (84.5% identification accuracy) and consumes 39% less time for the identification of speaker.

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Section: Dialectical Variations In Pashtomentioning

confidence: 99%

Section: Afghanistan Pakistan Border Main Citiesmentioning

confidence: 99%

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A Robust Approach for Speaker Identification Using Dialect Information

Shah

Moinuddin²,

Khan

2022

Applied Computational Intelligence and Soft Computing

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Development of a regional voice dataset and speaker classification based on machine learning

et al. 2021

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At present, voice biometrics are commonly used for identification and authentication of users through their voice. Voice based services such as mobile banking, access to personal devices, and logging into social networks are the common examples of authenticating users through voice biometrics. In Pakistan, voice-based services are very common in banking and mobile/cellular sector, however, these services do not use voice features to recognize customers. Therefore, the chance to use these services with false identity is always high. It is essential to design a voice-based recognition system to minimize the risk of false identity. In this paper, we developed regional voice datasets for voice biometrics, by collecting voice data in different local accents of Pakistan. Although, there is a global need for voice biometrics especially when voice-based services are common, however, this paper uses Pakistan as a use case to show how to build regional voice dataset for voice biometrics. To build voice dataset, voice samples were recorded from 180 male and female speakers with two languages English and Urdu in form of five regional accents. Mel Frequency Cepstral Coefficient (MFCC) features were extracted from the collected voice samples to train Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF) and K-nearest neighbor (KNN) classifiers. The results indicate that ANN outperformed SVM, RF and KNN by achieving 88.53% and 86.58% recognition accuracy on both datasets respectively.

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A Pashtu speakers database using accent and dialect approach

Cited by 2 publications

References 0 publications

A Robust Approach for Speaker Identification Using Dialect Information

A Robust Approach for Speaker Identification Using Dialect Information

Development of a regional voice dataset and speaker classification based on machine learning

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