Emirati-accented speaker identification in each of neutral and shouted talking environments

Shahin, Ismail; Nassif, Ali Bou; Bahutair, Mohammed

doi:10.1007/s10772-018-9502-0

Cited by 16 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mel Frequency Cepstral Coefficient (MFCC) is the most commonly used feature extraction techniques in speaker [25], [26] and emotion [27], [28] recognition. MFCC gives the logarithmic perception of onset and pitch of the human auditory system.…”

Section: Feature Extractionmentioning

confidence: 99%

Emotion Recognition Using Hybrid Gaussian Mixture Model and Deep Neural Network

2019

Self Cite

View full text Add to dashboard Cite

This paper aims at recognizing emotions for a text-independent and speaker-independent emotion recognition system based on a novel classifier, which is a hybrid of a cascaded Gaussian mixture model and deep neural network (GMM-DNN). This hybrid classifier has been assessed for emotion recognition on ''Emirati speech database (Arabic United Arab Emirates Database)'' with six different emotions. The sequential GMM-DNN classifier has been contrasted with support vector machines (SVMs) and multilayer perceptron (MLP) classifiers, and its performance accuracy is indexed at 83.97%, while the other two perform at 80.33% and 69.78% using SVMs and MLP, respectively. These results demonstrate that the hybrid classifier significantly gives higher emotion recognition accuracy than SVMs and MLP classifiers. Our GMM-DNN model yields the results similar to those obtained by human judges in a subjective assessment context. Also, the performance of the classifier has been tested using two distinct emotional databases and in normal and noisy talking conditions. The dominant signal mask provided by the hybrid classifier offers better system performance in the presence of noisy signals. INDEX TERMS Deep neural network, emotion recognition, Gaussian mixture model.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Emotion Recognition Using Hybrid Gaussian Mixture Model and Deep Neural Network

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Shahin et al [30] proposed a novel classifier called the cascaded GMM deep neural network (GMM-DNN) to enhance text-independent SI performance using two corpora: the Emirati speech database and ''speech under simulated and actual stress'' English database. Additionally, Shahin et al [29] identified Emirati-accented speakers in neutral talking and shouting environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Speaker Identification in Different Emotional States in Arabic and English

et al. 2020

View full text Add to dashboard Cite

Speaker recognition is an important application of digital speech processing. However, a major challenge degrading the robustness of speaker-recognition systems is variation in the emotional states of speakers, such as happiness, anger, sadness, or surprise. In this paper, we propose a speaker recognition system corresponding to three states, namely emotional, neutral, and with no consideration for a speaker's state (i.e., the speaker can be in an emotional state or neutral state), for two languages: Arabic and English. Additionally, cross-language speaker recognition was applied in emotional, neutral, and (emotional + neutral) states. Convolutional neural network and long short-term memory models were used to design a convolutional recurrent neural network (CRNN) main system. We also investigated the use of linearly spaced spectrograms as speech-feature inputs. The proposed system utilizes the KSUEmotions, emotional prosody speech and transcripts, WEST POINT, and TIMIT corpora. The CRNN system exhibited accuracies as high as 97.4% and 97.18% for Arabic and English emotional speech inputs, respectively, and 99.89% and 99.4% for Arabic and English neutral speech inputs, respectively. For the cross-language program, the overall CRNN system accuracy was as high as 91.83%, 99.88%, and 95.36% for emotional, neutral, and (emotional + neutral) states, respectively.

show abstract

“…where i Ψ is the probability of a state si at time t = 1 and aijk is the probability of the transition from a state si to a state sk at time t = 3. Prob t t 1 t 2 t 3 t 3 3 2 1 1 1 (5) Supplementary information about HMM3s can be found in reference [14]".…”

Section: Third-order Hidden Markov Modelsmentioning

confidence: 99%

Emirati-Accented Speaker Identification in Stressful Talking Conditions

Shahin

Nassif

2019

2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA)

Self Cite

View full text Add to dashboard Cite

This research is dedicated to improving "textindependent Emirati-accented speaker identification performance in stressful talking conditions" using three distinct classifiers: "First-Order Hidden Markov Models (HMM1s), Second-Order Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s)". The database that has been used in this work was collected from 25 per gender Emirati native speakers uttering eight widespread Emirati sentences in each of neutral, shouted, slow, loud, soft, and fast talking conditions. The extracted features of the captured database are called "Mel-Frequency Cepstral Coefficients (MFCCs)". Based on HMM1s, HMM2s, and HMM3s, average Emirati-accented "speaker identification accuracy in stressful conditions" is 58.6%, 61.1%, and 65.0%, respectively. The achieved "average speaker identification accuracy in stressful conditions based on HMM3s" is so similar to that attained in "subjective assessment by human listeners".

show abstract

Emirati-accented speaker identification in each of neutral and shouted talking environments

Cited by 16 publications

References 33 publications

Emotion Recognition Using Hybrid Gaussian Mixture Model and Deep Neural Network

Emotion Recognition Using Hybrid Gaussian Mixture Model and Deep Neural Network

Speaker Identification in Different Emotional States in Arabic and English

Emirati-Accented Speaker Identification in Stressful Talking Conditions

Contact Info

Product

Resources

About