Fast binary features for speaker recognition in embedded systems

Laptik, R.; Sledevič, Tomyslav

doi:10.1109/estream.2017.7950317

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The experimental results of the study achieved 93% classification accuracy. Laptik, et al [26] and Prasad, et al [27] evaluated MFFC features and a GMM classifier to classify 50 and 138 speakers from the CMU and YOHO datasets, respectively. The results of the experiment that used the proposed feature extraction methods exhibited 86% and 88% classification accuracy.…”

Section: A Related Studiesmentioning

confidence: 99%

Text-Independent Speaker Identification Through Feature Fusion and Deep Neural Network

et al. 2020

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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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Section: A Related Studiesmentioning

confidence: 99%

Text-Independent Speaker Identification Through Feature Fusion and Deep Neural Network

et al. 2020

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Spectral warping based data augmentation for low resource children’s speaker verification

Kathania,

Kadyan,

Kadiri

et al. 2023

Multimed Tools Appl

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In this paper, we present our effort to develop an automatic speaker verification (ASV) system for low resources children’s data. For the children’s speakers, very limited amount of speech data is available in majority of the languages for training the ASV system. Developing an ASV system under low resource conditions is a very challenging problem. To develop the robust baseline system, we merged out of domain adults’ data with children’s data to train the ASV system and tested with children’s speech. This kind of system leads to acoustic mismatches between training and testing data. To overcome this issue, we have proposed spectral warping based data augmentation. We modified adult speech data using spectral warping method (to simulate like children’s speech) and added it to the training data to overcome data scarcity and mismatch between adults’ and children’s speech. The proposed data augmentation gives 20.46% and 52.52% relative improvement (in equal error rate) for Indian Punjabi and British English speech databases, respectively. We compared our proposed method with well known data augmentation methods: SpecAugment, speed perturbation (SP) and vocal tract length perturbation (VTLP), and found that the proposed method performed best. The proposed spectral warping method is publicly available at https://github.com/kathania/Speaker-Verification-spectral-warping.

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Fast binary features for speaker recognition in embedded systems

Cited by 2 publications

References 7 publications

Text-Independent Speaker Identification Through Feature Fusion and Deep Neural Network

Text-Independent Speaker Identification Through Feature Fusion and Deep Neural Network

Spectral warping based data augmentation for low resource children’s speaker verification

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