Speaker recognition method based on deep residual network and improved power normalized Cepstral coefficients features

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

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A Proposed Speaker Recognition Method B Based on Long-Term Voice Features and Fuzzy Logic

Engineering and Technology Journal ◽

10.30684/etj.v39i1b.343 ◽

2021 ◽

Vol 39 (1B) ◽

pp. 1-10

Author(s):

Iman H. Hadi ◽

Alia K. Abdul-Hassan

Keyword(s):

Fuzzy Logic ◽

Speaker Recognition ◽

Recognition Accuracy ◽

Inner Product ◽

Maximum Frequency ◽

Recognition Method ◽

Data Set ◽

Zero Crossing ◽

Zero Crossing Rate

Speaker recognition depends on specific predefined steps. The most important steps are feature extraction and features matching. In addition, the category of the speaker voice features has an impact on the recognition process. The proposed speaker recognition makes use of biometric (voice) attributes to recognize the identity of the speaker. The long-term features were used such that maximum frequency, pitch and zero crossing rate (ZCR). In features matching step, the fuzzy inner product was used between feature vectors to compute the matching value between a claimed speaker voice utterance and test voice utterances. The experiments implemented using (ELSDSR) data set. These experiments showed that the recognition accuracy is 100% when using text dependent speaker recognition.

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Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i2.pp782-789 ◽

2020 ◽

Vol 18 (2) ◽

pp. 782

Author(s):

Musab T. S. Al-Kaltakchi ◽

Haithem Abd Al-Raheem Taha ◽

Mohanad Abd Shehab ◽

Mohamed A.M. Abdullah

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Speaker Identification ◽

Gaussian Mixture ◽

Identification Accuracy ◽

Identification System ◽

Good Representation ◽

Mel Frequency Cepstral Coefficients ◽

Normalization Methods ◽

Cepstral Coefficients

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

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The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool

Applied Sciences ◽

10.3390/app9235064 ◽

2019 ◽

Vol 9 (23) ◽

pp. 5064 ◽

Cited By ~ 5

Author(s):

Marco Civera ◽

Matteo Ferraris ◽

Rosario Ceravolo ◽

Cecilia Surace ◽

Raimondo Betti

Keyword(s):

Experimental Data ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Speech Processing ◽

Speaker Recognition ◽

Vibration Analysis ◽

Monitoring Tool ◽

Mel Frequency Cepstral Coefficients ◽

Structural Health ◽

Cepstral Coefficients

Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques for the same aims. The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. These features are very closely related to MFCCs, but provide interesting and useful additional values, such as e.g., improved robustness with respect to noise. The goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. Promising results from both numerical and experimental data were obtained.

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