Efficient window for monolingual and crosslingual speaker identification using MFCC

Nagaraja, B. G.; Jayanna, H. S.

doi:10.1109/icaccs.2013.6938702

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…Voices flow henceforth through the vocal track propagating through the throat and mouth. Speech tone is directly impacted by the shape of the vocal tacks and by the objects available inside the mouth such as the number of teeth [ 4 ]. A speech system is encountered for various numbers of challenges that are vital to the modelling process of the speaker.…”

Section: Introductionmentioning

confidence: 99%

Development of High Accuracy Classifier for the Speaker Recognition System

Al_Hassani

Atilla

Aydın

2021

Applied Bionics and Biomechanics

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Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), K -nearest neighbour (KNN), and support vector machine (SVM).

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Section: Introductionmentioning

confidence: 99%

Development of High Accuracy Classifier for the Speaker Recognition System

Al_Hassani

Atilla

Aydın

2021

Applied Bionics and Biomechanics

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“…Meanwhile, the SI community has always speculated the impact of language on those systems [28]. In fact, some studies investigate this topic [29,30], but they usually employ languages with common ancestry such as English and German, or even accent variations. Other limitations on these studies are the use of a small dataset, not providing a better description of how to split the dataset or any statistical tests performed.…”

Section: Literature Reviewmentioning

confidence: 99%

Phoneme analysis for multiple languages with fuzzy‐based speaker identification

Lima

Abreu

2022

IET Biometrics

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Most voice biometric systems are dependent on the language of the user. However, if the idea is to create an all-inclusive and reliable system that uses speech as its input, then they should be able to recognise people regardless of language or accent. Thus, this paper investigates the effects of languages on speaker identification systems and the phonetic impact on their performance. The experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. The Mel-Frequency Cepstrum Coefficients and its Deltas are extracted from those languages. Also, this paper expands the research study of fuzzy models in the speaker recognition field, using a Fuzzy C-Means and Fuzzy k-Nearest Neighbours and comparing them with k-Nearest Neighbours and Support Vector Machines. Results with more languages decreases the accuracy from 92% to 85.59%, but further investigation suggests it is caused by the number of classes. A phonetic investigation finds no relation between the phonemes and the results. Finally, fuzzy methods offer more flexibility and in some cases, even better results compared to their crisp version. Therefore, the biometric system presented here is not affected by multilingual environments.

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“…This method firstly decomposes the speech into 20-30 ms speech frames. For each frame, the cepstral coefficient can be calculated as follows [18]:…”

Section: Short Vector Extractionmentioning

confidence: 99%

Speaker Recognition Using Wavelet Packet Entropy, I-Vector, and Cosine Distance Scoring

Lei

She

2017

Journal of Electrical and Computer Engineering

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Today, more and more people have benefited from the speaker recognition. However, the accuracy of speaker recognition often drops off rapidly because of the low-quality speech and noise. This paper proposed a new speaker recognition model based on wavelet packet entropy (WPE), i-vector, and cosine distance scoring (CDS). In the proposed model, WPE transforms the speeches into short-term spectrum feature vectors (short vectors) and resists the noise. I-vector is generated from those short vectors and characterizes speech to improve the recognition accuracy. CDS fast compares with the difference between two i-vectors to give out the recognition result. The proposed model is evaluated by TIMIT speech database. The results of the experiments show that the proposed model can obtain good performance in clear and noisy environment and be insensitive to the low-quality speech, but the time cost of the model is high. To reduce the time cost, the parallel computation is used.

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Efficient window for monolingual and crosslingual speaker identification using MFCC

Cited by 10 publications

References 11 publications

Development of High Accuracy Classifier for the Speaker Recognition System

Development of High Accuracy Classifier for the Speaker Recognition System

Phoneme analysis for multiple languages with fuzzy‐based speaker identification

Speaker Recognition Using Wavelet Packet Entropy, I-Vector, and Cosine Distance Scoring

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