Proceedings ArticleDOI
Speaker Identification Using a Hybrid CNN-MFCC Approach
Aweem Ashar,Muhammad Shahid Bhatti,Usama Mushtaq +2 more
- pp 1-4
TLDR
A novel architecture is proposed using a convolutional neural network (CNN) and mel frequency cepstral coefficient (MFCC) to identify the speaker in a noisy environment to verify the effectiveness of this hybrid architecture.Abstract:
In this paper, a novel architecture is proposed using a convolutional neural network (CNN) and mel frequency cepstral coefficient (MFCC) to identify the speaker in a noisy environment. This architecture is used in a text-independent setting. The most important task in any text-independent speaker identification is the capability of the system to learn features that are useful for classification. We are using a hybrid feature extraction technique using CNN as a feature extractor combines with MFCC as a single set. For classification, we used a deep neural network which shows very promising results in classifying speakers. We made our dataset containing 60 speakers, each speaker has 4 voice samples. Our best hybrid model achieved an accuracy of 87.5%. To verify the effectiveness of this hybrid architecture, we use parameters such as accuracy and precision.read more
Citations
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Proceedings ArticleDOI
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks
Aneesh Muppidi,Martin Radfar +1 more
TL;DR: This paper proposed a quaternion convolutional neural network (QCNN) based speech emotion recognition (SER) model in which Mel-spectrogram features of speech signals are encoded in an RGB quaternions domain.
Journal ArticleDOI
Speaker identification based on Radon transform and CNNs in the presence of different types of interference for Robotic Applications
Amira Shafik,Ahmed Sedik,Basma Abd El-Rahiem,El-Sayed M. El-Rabaie,Ghada M. El Banby,Fathi E. Abd El-Samie,Fathi E. Abd El-Samie,Ashraf A. M. Khalaf,Oh-Young Song,Abdullah M. Iliyasu,Abdullah M. Iliyasu,Abdullah M. Iliyasu +11 more
TL;DR: A new approach to improve the accuracy of speaker identification in the presence of interference for robot control applications with a convolutional neural network (CNN) that achieves a high classification accuracy up to 97.5%, which is more than double the performance reported for some traditional methods that are used for speaker identification.
Journal ArticleDOI
Enhanced Indonesian Ethnic Speaker Recognition using Data Augmentation Deep Neural Network
TL;DR: After seeing the performance of this model, it can be concluded that Data Augmentation Deep Neural Network can improve the speaker's recognition performance using the Indonesian ethnic dataset.
Journal ArticleDOI
An optimum end-to-end text-independent speaker identification system using convolutional neural network
TL;DR: In this article , the authors proposed a new CNN for text-independent speaker identification inspired by the VGG-13 architecture with fewer parameters but an acceptable accuracy, which reduced the time complexity and memory cost of network training by using a short segment of each audio sample.
Journal ArticleDOI
A strong hybrid AdaBoost classification algorithm for speaker recognition
TL;DR: In this article, a hybrid adaptive boosting (AdaBoost) combined with a powerful ML classifier (Random Forest) is proposed to handle multi-class imbalanced speaker data classification.
References
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Proceedings ArticleDOI
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