Speaker recognition master thesis

Speaker Recognition using RBF Neural Network Trained LPC and MFCC Features - Free-Thesis
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This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features. Speaker recognition is a technique of identifying the person talking to a machine using the voice features and acoustics. It has multiple applications ranging in the fields of Human Computer Interaction (HCI), biometrics, security, and Internet of Things (IoT). SPEAKER RECOGNITION USING DEEP NEURAL NETWORKS WITH REDUCED COMPLEXITY. by. Vidya Thanda Setty, B.E. A thesis submitted to the Graduate Council of. Texas State University in partial fulfillment. of the requirements for the degree of. Master of Science. with a Major in Engineering. December Committee Members: Vishu Viswanathan, Chair. George Koutitas.

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This is to certify that the thesis titled, “Study of Speaker Recognition Systems” submitted by Ashish Kumar Panda (EC) and Amit Kumar Sahoo (EC) in partial fulfilments for the requirements for the award of Bachelor of Technology Degree in Electronics and Communication Engineering, National Institute of Technology, Rourkela is an authentic work carried out by them is under my supervision. Aug 28,  · The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others. The speaker recognition perspective enables readers to apply machine learning techniques to address practical issues (e.g., robustness under adverse acoustic environments and domain mismatch) when deploying speaker recognition systems. The theories and practices of speaker recognition are tightly connected in the book.

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Aug 28,  · The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others. In this thesis the operation of the speaker recognition systems is described and the state of the art of the main working blocks is studied. All the research papers looked through can be found in the References. As voice is unique to the individual, it has emerged as a viable authentication method. There. This is to certify that the thesis titled, “Study of Speaker Recognition Systems” submitted by Ashish Kumar Panda (EC) and Amit Kumar Sahoo (EC) in partial fulfilments for the requirements for the award of Bachelor of Technology Degree in Electronics and Communication Engineering, National Institute of Technology, Rourkela is an authentic work carried out by them is under my supervision.

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Aug 28,  · The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others. This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features. Speaker Recognition is a process of automatically recognizing who is speaking on the basis of the individual information included in speech waves. Speaker Recognition is one of the most useful biometric recognition techniques in this world where insecurity is a major threat.

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SPEAKER RECOGNITION USING DEEP NEURAL NETWORKS WITH REDUCED COMPLEXITY. by. Vidya Thanda Setty, B.E. A thesis submitted to the Graduate Council of. Texas State University in partial fulfillment. of the requirements for the degree of. Master of Science. with a Major in Engineering. December Committee Members: Vishu Viswanathan, Chair. George Koutitas. This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features. This is to certify that the thesis titled, “Study of Speaker Recognition Systems” submitted by Ashish Kumar Panda (EC) and Amit Kumar Sahoo (EC) in partial fulfilments for the requirements for the award of Bachelor of Technology Degree in Electronics and Communication Engineering, National Institute of Technology, Rourkela is an authentic work carried out by them is under my supervision.