Results 1 to 10 of about 1,079 (133)
IEEE Access, 2017 Environmental noise and reverberation conditions severely degrade the performance of forensic speaker verification. Robust feature extraction plays an important role in improving forensic speaker verification performance.
Ahmed Kamil Hasan Al-Ali +2 more
exaly +3 more sources
IEEE Access, 2020 Feature extraction is an essential part of automatic speech recognition (ASR) to compress raw speech data and enhance features, where conventional implementation methods based on the digital domain have encountered energy consumption and processing speed
Qin Li, Yuze Yang, Tianxiang Lan
exaly +3 more sources
Significance of chirp MFCC as a feature in speech and audio applications
Computer Speech and LanguageA novel feature, based on the chirp z-transform, that offers an improved representation of the underlying true spectrum is proposed. This feature, the chirp MFCC, is derived by computing the Mel frequency cepstral coefficients from the chirp magnitude spectrum, instead of the Fourier transform magnitude spectrum.
T Nagarajan, S Johanan Joysingh
exaly +3 more sources
Learnable MFCCs for Speaker Verification [PDF]
2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021 Accepted to ISCAS ...
Xuechen Liu 0001 +2 more
openaire +4 more sources
Comparative Study of different types of RNN in Speech Classification [PDF]
The Egyptian Journal of Language Engineering, 2021 This paper introduces different models for pre-processing classification and their performance in Automatic Speech Recognition system. Different Recurrent Neural Network (RNN) architectures have been tested for this problem, such as RNN cells (RNN ...
Tarek Said, Amr Gody, Ayat Ragheb
doaj +1 more source
Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier [PDF]
ITM Web of Conferences, 2022 Parkinson’s disease (PD) is one of the neurodegenerative diseases. The neuronal loss caused by this disease leads to symptoms such as lack of initiative, depressive states, psychological disorders, and impairment of cognitive functions as well as voice ...
Boualoulou N. +3 more
doaj +1 more source
Optimizing MFCC parameters for the automatic detection of respiratory diseases
Applied AcousticsVoice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) is widely used for automatic analysis, with MFCC extraction commonly relying on default parameters.
Yuyang Yan, , Visara Urovi
exaly +4 more sources
Arabic Speaker Identification System Using Multi Features [PDF]
Engineering and Technology Journal, 2020 The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the ...
Rawia Mohammed, Nidaa Hassan, Akbas Ali
doaj +1 more source
Mel Frequency Cepstral Coefficient and its Applications: A Review
IEEE Access, 2022 Feature extraction and representation has significant impact on the performance of any machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to model features of audio signal and is widely used in various fields.
Zrar Kh. Abdul +1 more
doaj +1 more source
Enhancing Performance of End-to-End Gujarati Language ASR using combination of Integrated Feature Extraction and Improved Spell Corrector Algorithm [PDF]
ITM Web of Conferences, 2023 A number of intricate deep learning architectures for effective End-to-End (E2E) speech recognition systems have emerged due to recent advancements in algorithms and technical resources.
Bhagat Bhavesh, Dua Mohit
doaj +1 more source