Speaker Recognition Using Marathi (Varhadi) Language

Bansod, Nagsen S.; Dadhade, Siddharth B.; Kawathekar, Seema S.; Kale, K. V.

doi:10.1109/icica.2014.92

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Maharashtra covers a larger geographical area having 34 different districts. According to different regions of Maharashtra, districts are divided into Marathwada, Paschim Maharashtra, Vidarbha, Konkan, and Khandesh [4]. Dialects are variations of the same language and are specific to geographical regions or social groups [7].…”

Section: Marathi Dialect Recognitionmentioning

confidence: 99%

Text-Independent Automatic Dialect Recognition of Marathi Language using Spectro-Temporal Characteristics of Voice

Mulla¹,

Kumar²

2022

IJRITCC

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Text-independent dialect recognition system is proposed in this paper for Marathi language. India is rich in language varieties. Each language in turn has its unique dialect variations. Maharashtra has Marathi as official language and for Goa it is a co-official language . In literature there are very few studies available for Indian language recognition and then their respective dialect recognition. So research work available for regional languages such as Marathi is extremely limited. As a part of research work, an attempt is made to generate a case study of a low resourced Marathi language dialect recognition system. The study was carried out using Marathi speech data corpus provided by Linguistic Data Consortium for Indian Language (LDC- IL). This corpus includes four major dialects of Marathi speakers. The efficiency and performance evaluation of the explored spectral (rhythmic) and temporal features are carried out to perform classification tasks. We investigated the performance of six different classifiers; K-nearest neighbor (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) classifier , Stochastic Gradient Descent (SGD) classifier and Ridge Classifier (RC). Experimental results have demonstrated that the RC classifier worked well with 84.24% of accuracy for fifteen spectral and temporal features. With twelve MFCCs it has been observed that SGD has outperformed among all classifiers with accuracy of 80.63%. For further study, a prominent feature subset as a part of dimensionality reduction has been identified using chi square, mutual information and ANOVA-f test. In this chi-square based feature extraction method has proven to be the best over over mutual information and ANOVA f-test.

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Section: Marathi Dialect Recognitionmentioning

confidence: 99%

Text-Independent Automatic Dialect Recognition of Marathi Language using Spectro-Temporal Characteristics of Voice

Mulla¹,

Kumar²

2022

IJRITCC

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“…Marathi voice based system in (Bansod, Dadhade, Kawathekar, & Kale, 2014) has performed a recognition rate of 88% on database PHASE-II. A database with various language speakers like Arabic speakers, Mandarin speakers, Russian speakers, and Spanish speakers is used for cross-lingual authentication in (J.…”

Section: Voice Featuresmentioning

confidence: 99%

Authentication systems: principles and threats

Abdulkader¹,

Atia²,

Mostafa³

2015

CIS

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Identity manipulation is considered a serious security issue that has been enlarged with the spread of automated systems that could be accessed either locally or remotely. Availability, integrity, and confidentiality represent the basic requirements that should be granted for successful authentication systems. Personality verification has taken multiple forms depending on different possession types. They are divided into knowledge based, token based, and biometric based authentication. The permanent ownership to the human being has increased the chances of deploying biometrics based authentication in highly secure systems. It includes capturing the biological traits, which are physiological or behavioral, extracting the important features and comparing them to the previously stored features that belong to the claimed user. Various kinds of attacks aim to take down the basic requirements at multiple points. This paper describes different types of authentication along with their vulnerable points and threatening attacks. Then it provides more details about the biometric system structure as well as examples of distinguishing biological characteristics, organized by their locations. It shows the performance results of various biometric systems along with the deployed algorithms for different components.

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“…Kamble V. et al[16] have presented emotion recognition system for Marathi Language. The system recognizes emotions such as happy, Bansod N. et al[1] have proposed the system for Varhadi language which is also another dialect of Marathi language. MFCC and LPC features extraction techniques have been used for speaker recognition.…”

mentioning

confidence: 99%

Speakar Independent Isolated Word Recognition using HTK for Varhadi a Dialect of Marathi

Patil,

Patil

2020

IJEAT

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Abstract: Speech recognition is widely used in the computer science to make well-organized communication between humans and computers. This paper addresses the problem of speech recognition for Varhadi, the regional language of the state of Maharashtra in India. Varhadi is widely spoken in Maharashtra state especially in Vidharbh region. Viterbi algorithm is used to recognize unknown words using Hidden Markov Model (HMM). The dataset is developed to train the system consists of 83 isolated Varhadi words. A Mel frequency cepstral coefficient (MFCCs) is used as feature extraction to perform the acoustical analysis of speech signal. Word model is implemented in speaker independent mode for the proposed varhadi automatic speech recognition system (V-ASR). The training and test dataset consist of isolated words uttered by 8 native speakers of Varhadi language.The V-ASR system has recognized the Varhadi words satisfactorily with 92.77%. recognition performance.

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Speaker Recognition Using Marathi (Varhadi) Language

Cited by 3 publications

References 5 publications

Text-Independent Automatic Dialect Recognition of Marathi Language using Spectro-Temporal Characteristics of Voice

Text-Independent Automatic Dialect Recognition of Marathi Language using Spectro-Temporal Characteristics of Voice

Authentication systems: principles and threats

Speakar Independent Isolated Word Recognition using HTK for Varhadi a Dialect of Marathi

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