Spectral-temporal receptive fields and MFCC balanced feature extraction for robust speaker recognition

Wang, Jia-Ching; Wang, Chien-Yao; Chin, Yu-Hao; Liu, Yuting; Chen, En-Ting; Chang, Pao-Chi

doi:10.1007/s11042-016-3335-0

Cited by 22 publications

(5 citation statements)

References 19 publications

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“…Pitch-synchronous SFF spectrogram can be used in various other applications such as speaker identification [18], [36], speaker verification [39], audio classification [41] etc. The SFF output has high SNR values of speech in the time-frequency domain.…”

Section: Discussionmentioning

confidence: 99%

Pitch-synchronous single frequency filtering spectrogram for speech emotion recognition

Gupta

Fahad

Deepak

2020

Multimed Tools Appl

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Convolutional neural networks (CNN) are widely used for speech emotion recognition (SER). In such cases, the short time fourier transform (STFT) spectrogram is the most popular choice for representing speech, which is fed as input to the CNN. However, the uncertainty principles of the short-time Fourier transform prevent it from capturing time and frequency resolutions simultaneously. On the other hand, the recently proposed single frequency filtering (SFF) spectrogram promises to be a better alternative because it captures both time and frequency resolutions simultaneously. In this work, we explore the SFF spectrogram as an alternative representation of speech for SER. We have modified the SFF spectrogram by taking the average of the amplitudes of all the samples between two successive glottal closure instants (GCI) locations. The duration between two successive GCI locations gives the pitch, motivating us to name the modified SFF spectrogram as pitch-synchronous SFF spectrogram. The GCI locations were detected using zero frequency filtering approach. The proposed pitch-synchronous SFF spectrogram produced accuracy values of 63.95% (unweighted) and 70.4% (weighted) on the IEMOCAP dataset. These correspond to an improvement of +7.35% (unweighted) and +4.3% (weighted) over state-of-the-art result on the STFT sepctrogram using CNN. Specially, the proposed method recognized 22.7% of the happy emotion samples correctly, whereas this number was 0% for state-of-the-art results. These results also promise a much wider use of the proposed pitch-synchronous SFF spectrogram for other speech-based applications.

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Section: Discussionmentioning

confidence: 99%

Pitch-synchronous single frequency filtering spectrogram for speech emotion recognition

Gupta

Fahad

Deepak

2020

Multimed Tools Appl

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“…To evaluate the performance of the proposed speaker recognition system, experiments on 36speaker recognition were conducted. Comparing with the MFCC baseline, the proposed feature set increases the speaker recognition rates by 3.85 % and 18.49 % on clean and noisy speeches, respectively (11) .…”

Section: Introductionmentioning

confidence: 95%

Mel Frequency Cepstral Coefficients based Bacterial Foraging Optimization with DNN-RBF for Speaker Recognition

Subhashini¹,

Ram²,

Rao³

2021

IJST

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Objectives: To improve the accuracy and to reduce the time complexity of the Speaker Recognition system using Mel-Frequency Cepstral Coefficients (MFCCs) and Bacterial Foraging optimization (BFO) with DNN -RBF. Method: The MFCCs of each speech sample are derived by pre-processing the audio speech signal. The features are optimized with BFO algorithm. Finally, the probability score for each speaker is generated to identify the speaker. Then the features are classified towards the target speaker using DNN-RBF. For the proposed MBFOB speaker recognition function, the TIMIT read corpus is used. It contains a total of 6300 phrases, 10 phrases each. Findings: the identity of user is validated in the fields of authentication and surveillance for recognition of speaker. By using the audio speech signal, features are extracted. This paper suggests an MBFOB solution based on Mel-frequency Cepstral Coefficients and DNN-RBF with BFO, for the identification of speakers. The speech utterance from the TIMIT data corpus is preprocessed to obtain MFCC feature vectors DNN-RBF is used for the purpose of classifying the speaker and the feature vectors in the output layers are optimized with Bacterial Foraging optimization. Finally, the scores for each speaker are calculated to identify the speaker. Different output metrics like EER, DCF, Cavg and accuracy are used to test the proposed speaker recognition technique. The execution time of this proposed method is found to be lesser than the other existing methods. The experimental findings are contrasted with other current methods and it shows the efficiency of our approach. Novelty: A novel MFCC-based Bacterial Foraging Optimization with Deep Neural Network-Radial Basis Function (DNN-RBF) for identification of exact speaker is proposed in this study.

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“…Several studies have been conducted using both conventional machine learning and deep learning methods to enhance the accuracy of speaker recognition systems under noisy and mismatched conditions. Some of the conventional machine learning methods include the Gaussian mixture model (GMM) [14] and support vector machine (SVM) [15]. These methods have been using handcrafted features for speaker recognition and other speech analysis purposes.…”

Section: Introductionmentioning

confidence: 99%

Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit

Anito,

Srinivasagan,

Jifara

et al. 2024

IJ-AI

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Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.

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Spectral-temporal receptive fields and MFCC balanced feature extraction for robust speaker recognition

Cited by 22 publications

References 19 publications

Pitch-synchronous single frequency filtering spectrogram for speech emotion recognition

Pitch-synchronous single frequency filtering spectrogram for speech emotion recognition

Mel Frequency Cepstral Coefficients based Bacterial Foraging Optimization with DNN-RBF for Speaker Recognition

Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit

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