Dilated Convolution and MelSpectrum for Speaker Identification using Simple Deep Network

Pentapati, Hema Kumar; Sridevi, K.

doi:10.1109/icaccs54159.2022.9785196

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…These features harbor discriminative properties tailored to individual utterances, encapsulating their intrinsic characteristics. Regarded as a data reduction step, feature extraction condenses lengthy utterances into compact data that encapsulates the core attributes of the speaker [1].…”

Section: Speaker Identification Processmentioning

confidence: 99%

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Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Hassan,

Neyazi,

Seddeq

et al. 2024

PLoS ONE

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This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.

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Section: Speaker Identification Processmentioning

confidence: 99%

“…Speech has been a fundamental mode of human communication since ancient times, arising from vocal tract excitation. Physiological attributes contributing to speech differ across individuals, including variations in vocal tract size, shape, vocal fold structure, velum, and nasal cavity, especially between genders [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Hassan,

Neyazi,

Seddeq

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Across the several methods in Table 2, the proposed method improves the accuracy by 7.61% compared to [22] baseline method for 10 speakers. The accuracy of the proposed method further improves by nearly 8% and 14% compared to the baseline methods [27] and [6] respectively. The proposed work does not introduce computational complexity since extracting the logmelspectrum and excitation features have been done offline.…”

mentioning

confidence: 94%

“…Speaker recognition can be divided into text-dependent and Textindependent systems. Text-dependent modules work with the same set of phrases in training and testing [6]. Whereas in text-independent systems, there are no such limitations on text phrases in training and testing.…”

Section: Introductionmentioning

confidence: 99%

Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

Pentapati

Sridevi

2023

Int. j. electr. comput. eng. syst. (Online)

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There are various challenges in identifying the speakers accurately. The Extraction of discriminative features is a vital task for accurate identification in the speaker identification task. Nowadays, speaker identification is widely investigated using deep learning. The complex and noisy speech data affects the performance of Mel Frequency Cepstral Coefficients (MFCC); hence, MFCC fails to represent the speaker characteristics accurately. In this proposed work, a novel text-independent speaker identification system is developed to enhance the performance by fusion of Log-MelSpectrum and excitation features. The excitation information is obtained due to the vibration of vocal folds, and it is represented using Linear Prediction (LP) residual. The various types of features extracted from the excitation are residual phase, sharpness, Energy of Excitation (EoE), and Strength of Excitation (SoE). The extracted features were processed with the dilated convolution neural network (dilated CNN) to fulfill the identification task. The extensive evaluation showed that the fusion of excitation features gives better results than the existing methods. The accuracy reaches 94.12% for 11 complex classes and 91.34% for 80 speakers, and Equal Error Rate (EER) is reduced to 1.16% for the proposed model. The proposed model is tested with the Librispeech corpus using Matlab 2021b tool, outperforming the existing baseline models. The proposed model achieves an accuracy improvement of 1.34% compared to the baseline system.

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Log-MelSpectrum and Excitation features based Speaker Identification using Deep Learning

Pentapati

Sridevi

2022

2022 International Conference on Industry 4.0 Technology (I4Tech)

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Dilated Convolution and MelSpectrum for Speaker Identification using Simple Deep Network

Cited by 3 publications

References 14 publications

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

Log-MelSpectrum and Excitation features based Speaker Identification using Deep Learning

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