Automatic Speaker Recognition System based on Machine Learning Algorithms

Mokgonyane, Tumisho Billson; Sefara, Tshephisho Joseph; Modipa, Thipe I.; Mogale, Mercy Mosibudi; Manamela, Madimetja Jonas; Manamela, Phuti John

doi:10.1109/robomech.2019.8704837

Cited by 27 publications

(7 citation statements)

References 21 publications

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“…The model worked well for datasets with or without noise and produced the same accuracy for both features. In [17] a new LSTM model was proposed for text dependent speaker verification task, which used the d-vectors generated from the audio file for the classification task and achieved an accuracy of 96.9% for the proposed model.In their proposed model, the authors of [18] test the classification of both textdependent and text-independent scenarios for speaker classification based on CNN. They obtain a higher accuracy for text-dependent speaker classification rather than text-independent speaker classification.…”

Section: Literature Reviewmentioning

confidence: 99%

novel voiceprint using ensembled Mel-Chromagram for speaker recognition

Banuroopa

Priyaa

2022

ijhs

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This research paper proposes a novel voiceprint generation methodology for recognizing the speakers registered in a system. The proposed methodology is a keyword-dependent closed set speaker classification task. The features used are Mel-Spectrogram, Chromagram, MFCC and a new ensembled feature called Mel-Chroma. Mel-Chroma is generated with the combination of Mel-spectrogram and Chromagram. The Mel-Chroma spectrogram generated is converted into a binary image by using the average as the threshold. The recurrent neural network model LSTM is used for the classification task and the dataset used is FSDD. The proposed method has a higher accuracy than the state-of-art methods for the specific task. The accuracy obtained for the classification of speakers using a binary Mel-Chroma voiceprint is 98.33%.

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Section: Literature Reviewmentioning

confidence: 99%

novel voiceprint using ensembled Mel-Chromagram for speaker recognition

Banuroopa

Priyaa

2022

ijhs

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“…In addition, voice and image processing is performed in real-time, and therefore notifications to the users will be more effective. To correctly perform both voice and image processing, the application uses deep learning techniques [29] that will help us have a more accurate detection. Finally, the proposed system ensures users' privacy since it does not collect any information from the terminal and does not need Internet connection nor GPS coordinates.…”

Section: Face Detection and Recognition With Mobile Phonesmentioning

confidence: 99%

A new system to detect coronavirus social distance violation

Botella-Campos

Gacía-Navas

Rego

et al. 2021

IJECE

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In this paper, a novel solution to avoid new infections is presented. Instead of tracing users’ locations, the presence of individuals is detected by analysing the voices, and people’s faces are detected by the camera. To do this, two different Android applications were implemented. The first one uses the camera to detect people’s faces whenever the user answers or performs a phone call. Firebase Platform will be used to detect faces captured by the camera and determine its size and estimate their distance to the phone terminal. The second application uses voice biometrics to differentiate the users’ voice from unknown speakers and creates a neural network model based on 5 samples of the user’s voice. This feature will only be activated whenever the user is surfing the Internet or using other applications to prevent undesired contacts. Currently, the patient’s tracking is performed by geolocation or by using Bluetooth connection. Although face detection and voice recognition are existing methods, this paper aims to use them and integrate both in a single device. Our application cannot violate privacy since it does not save the data used to carry out the detection and does not associate this data to people.

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“…Machine learning and deep learning models are increasingly being used to diagnose disease from medical images [10], natural language processing [11], sentiment analysis [12], computer vision [13], speech recognition [14], predictive analytics [15], data analytics [16], and to provide patient-centered care [17]. The advantage of the novel deep learning models such as convoluted neural networks (CNN) is that the development of the model does not require the time-consuming manual tagging of images by a healthcare professional.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Autism in Children Using Transfer Learning

Ghazal¹,

Munir²,

Abbas³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Autism spectrum disorder (ASD) is a challenging and complex neurodevelopment syndrome that affects the child's language, speech, social skills, communication skills, and logical thinking ability. The early detection of ASD is essential for delivering effective, timely interventions. Various facial features such as a lack of eye contact, showing uncommon hand or body movements, babbling or talking in an unusual tone, and not using common gestures could be used to detect and classify ASD at an early stage. Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial features. A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet (ASDDTLA) model. Our model achieved a detection accuracy of 87.7% and performed better than other established ASD detection models. Therefore, this model could facilitate the early detection of ASD in clinical practice.

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Automatic Speaker Recognition System based on Machine Learning Algorithms

Cited by 27 publications

References 21 publications

novel voiceprint using ensembled Mel-Chromagram for speaker recognition

novel voiceprint using ensembled Mel-Chromagram for speaker recognition

A new system to detect coronavirus social distance violation

Early Detection of Autism in Children Using Transfer Learning

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