Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit (original) (raw)
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The performance of speaker recognition is very well in a clean dataset or without mismatch between training and test set. However, the performance is degraded with noise, channel variation, physical and behavioral changes with the speaker. The studies confirmed that the features which represent speech in the Equal Rectangular Band (ERB) scale are more robust than Mel Scale at low Signal to Noise Ratio (SNR) level. Gammatone Frequency Cepstral Coefficient (GFCC) which represents speech in ERB scale is widely used in classical machine learning based speaker recognition at noisy conditions. Recently, deep learning models are widely applied in speaker recognition and show better performance than classical machine learning. Previous deep learning based speaker recognition models used Mel Spectrogram as an input rather than hand crafted features. However, the performance of Mel spectrogram drastically degraded at low SNR level because Mel Spectrogram represents speech in Mel Scale. Cochle...
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Speaker identification refers to the process of recognizing human voice using artificial intelligence techniques. Speaker identification technologies are widely applied in voice authentication, security and surveillance, electronic voice eavesdropping, and identity verification. In the speaker identification process, extracting discriminative and salient features from speaker utterances is an important task to accurately identify speakers. Various features for speaker identification have been recently proposed by researchers. Most studies on speaker identification have utilized short-time features, such as perceptual linear predictive (PLP) coefficients and Mel frequency cepstral coefficients (MFCC), due to their capability to capture the repetitive nature and efficiency of signals. Various studies have shown the effectiveness of MFCC features in correctly identifying speakers. However, the performances of these features degrade on complex speech datasets, and therefore, these features fail to accurately identify speaker characteristics. To address this problem, this study proposes a novel fusion of MFCC and time-based features (MFCCT), which combines the effectiveness of MFCC and time-domain features to improve the accuracy of text-independent speaker identification (SI) systems. The extracted MFCCT features were fed as input to a deep neural network (DNN) to construct the speaker identification model. Results showed that the proposed MFCCT features coupled with DNN outperformed existing baseline MFCC and time-domain features on the LibriSpeech dataset. In addition, DNN obtained better classification results compared with five machine learning algorithms that were recently utilized in speaker recognition. Moreover, this study evaluated the effectiveness of one-level and two-level classification methods for speaker identification. The experimental results showed that two-level classification presented better results than one-level classification. The proposed features and classification model for identifying a speaker can be widely applied to different types of speaker datasets.
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Automatic Speaker Recognition using Transfer Learning Approach of Deep Learning Models
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Speaker Recognition has been one of the most interesting yet challenging problem in the field of machine learning and artificial intelligence. It is used In areas of human voice authentication for security purpose and identifying a person from a group of speakers. It has been a grinding task to teach a machine the differences in human voices when people belong to different background like gender, language, accent.etc. In this paper, we use the deep learning approach to build and train two models : ANN and CNN; and compare their results. In former, the neural networks are fed on diverse extracted features from audio collection. The latter is a convolutional neural network trained on spectrograms. At last, we use transfer learning approach on both to get a viable output using less data.
Journal of Engineering and Applied Science, 2024
This paper studies the performance and reliability of deep learning-based speaker recognition schemes under various recording situations and background noise presence. The study uses the Speaker Recognition Dataset offered in the Kaggle website, involving audio recordings from different speakers, and four scenarios with various combinations of speakers. In the first scenario, the scheme achieves discriminating capability and high accuracy in identifying speakers without taking into account outside noise, having roughly one area under the ROC curve. Nevertheless, in the second scenario, with background noise added to the recording, accuracy decreases, and misclassifications increase. However, the scheme still reveals good discriminating power, with ROC areas ranging from 0.77 to 1.
2018
There has been much interest in new deep learning approaches for representing and extracting high-level features for audio processing. In this paper convolutional deep belief network was used to generate new speech features for textindependent speaker verification. Structure and parameters of a convolutional deep belief network were described. New high-level speech features were extracted using proposed method. Relevance of speaker verification systems for mobile authentication was considered. Gaussian mixture model and universal background model speaker verification system used for experiments was described. Speaker verification accuracy using extracted features was evaluated on a 50 speaker set and a result is presented. Different layers and combinations of layers of convolutional deep belief network were used as a features for a text-independent speaker verification. High level features extracted by convolutional deep belief network were illustrated and analyzed. Reasons of insuf...