IEEE Access, 2021 Automatic voice pathology detection enables objective assessment of pathologies that affect the voice production mechanism. Detection systems have been developed using the traditional pipeline approach (consisting of the feature extraction part and the ...
Mittapalle Kiran Reddy, Paavo Alku
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A REVIEW ON VOICE ACTIVITY DETECTION AND MEL-FREQUENCY CEPSTRAL COEFFICIENTS FOR SPEAKER RECOGNITION (TREND ANALYSIS) [PDF]
, 2016 Objective: The objective of this review article is to give a complete review of various techniques that are used for speech recognition purposes overtwo decades.Methods: VAD-Voice Activity Detection, SAD-Speech Activity Detection techniques are discussed
MAHALAKSHMI, P.
core +3 more sources
Wavelet based feature combination for recognition of emotions
Ain Shams Engineering Journal, 2018 In this paper, authors tried to develop reduced combinational features for emotional speech recognition. The spectral/cepstral features like wavelet coefficient, LPCC (linear prediction cepstral coefficient) and MFCC (mel-frequency cepstral coefficient ...
Hemanta Kumar Palo +1 more
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Speech and Language Markers of Bipolar Disorder: Challenges and Opportunities. [PDF]
Bipolar DisordABSTRACT Background Clinicians aspire to predict the emergence of Bipolar Disorder (BD) in a timely manner. To accomplish this, markers reflecting mental states that can be gathered non‐invasively and at large scale are needed. Here, we systematically evaluate evidence relating speech‐based markers to mood states in BD.
Zaher F +4 more
europepmc +2 more sources
Automatic Detection of Laryngeal Pathology on Sustained Vowels Using Short-Term Cepstral Parameters: Analysis of Performance and Theoretical Justification [PDF]
, 2008 The majority of speech signal analysis procedures for automatic detection of laryngeal pathologies mainly rely on parameters extracted from time domain processing.
Juan Ignacio Godino-Llorente +9 more
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SENTIMENT ANALYSIS ON SPEECH SIGNALS: LEVERAGING MFCC-LSTM TECHNIQUE FOR ENHANCED EMOTIONAL UNDERSTANDING [PDF]
Proceedings on Engineering SciencesThe analysis of emotions expressed in spoken language holds a pivotal role in human communication, artificial intelligence, and human-computer interaction. While emotion recognition in text has seen considerable advancements, recognizing emotional states
Suman Lata +2 more
doaj +1 more source
Nondestructive Determination of Maturity of the Monthong Durian by Mel-Frequency Cepstral Coefficients (MFCCs) and Neural Network [PDF]
Applied Mechanics and Materials, 2016 The challenging for buyers around the globe to identify good quality of Durian. For several kinds of Durian, it may be difficult for buyers to determine the Durian quality by appearance. The ability to select only good quality Durian without cutting or cleaving is useful because buyers will not waste money ordering undesirable Durian.This paper ...
Peerapol Khunarsa +3 more
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Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection [PDF]
, 2021 Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results ...
Heriyanto, Heriyanto
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Identifikasi Pola Suara Pada Bahasa Jawa Meggunakan Mel Frequency Cepstral Coefficients (MFCC)
JURNAL MEDIA INFORMATIKA BUDIDARMA, 2020 Voice Recognition is a process of developing systems used between computer and human. The purpose of this study is to find out the sound pattern of a person based on the spoken Javanese language. This study used the Mel Frequency Cepstral Coefficients (MFCC) method to solve the problem of feature extraction from human voices.
Istian Kriya Almanfaluti +1 more
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International Journal for Research in Applied Science and Engineering Technology, 2023 Abstract: Speaker recognition, a fundamental capability of software or hardware systems, involves receiving speech signals, identifying the speaker present in the speech signal, and subsequently recognizing the speaker for future interactions. This process emulates the cognitive task performed by the human brain. At its core, speaker recognition begins
V. Sai Nitin Varma, Abdul Majeed. K.K
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