Results 11 to 20 of about 10,198 (208)

Predicting Formant Frequencies from MFCC Vectors

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006
This work proposes a novel method of predicting formant frequencies from a stream of mel-frequency cepstral coefficients (MFCC) feature vectors. Prediction is based on modelling the joint density of MFCCs and formant frequencies using a Gaussian mixture ...
Jonathan Darch, Ben Milner, Xu Shao, Saeed Vaseghi, Qin Yan   +4 more
openaire   +2 more sources

Reconocimiento de patrones de habla usando MFCC y RNA [PDF]

Visión Electrónica, 2016
En este trabajo se presentan los resultados del diseño y desarrollo de un algoritmo basado en inteligencia artificial para el reconocimiento de patrones de vocablos del idioma español, utilizando Coeficientes Cepstrales en las Frecuencias de Mel o (MFCC),
Olga L. Ramos, Diego A. Rojas, Leonardo A. Góngora   +2 more
doaj   +4 more sources

Optimizing MFCC Parameters for Breathing Phase Detection

Sensors
Breathing phase detection is fundamental for various clinical and digital health applications, yet standard Mel Frequency Cepstral Coefficients (MFCCs) settings often limit classification performance. This study systematically optimized MFCC parameters, specifically the number of coefficients, frame length, and hop length, using a proprietary dataset ...
Assel K. Zhantleuova, Yerbulat K. Makashev, Nurzhan T. Duzbayev   +2 more
openaire   +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, Md. Sahidullah, Tomi Kinnunen   +2 more
openaire   +5 more sources

Formant prediction from MFCC vectors

, 2004
This work proposes a novel method of predicting formant frequencies from a stream of mel-frequency cepstral coefficients (MFCC) feature vectors. Prediction is based on modelling the joint density of MFCC vectors and formant vectors using a Gaussian ...
Darch, Jonathan, Milner, B.P., Shao, X.
core   +6 more sources

Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN

Journal of Information and Telecommunication, 2022
Analysing the human voice has always been a challenge to the engineering society for various purposes such as product review, emotional state detection, developing AI, and much more.
Mohammad Amaz Uddin, Refat Khan Pathan, Md Sayem Hossain, Munmun Biswas   +3 more
doaj   +1 more source

MSP-MFCC: Energy-Efficient MFCC Feature Extraction Method With Mixed-Signal Processing Architecture for Wearable Speech Recognition Applications

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, Huifeng Zhu, Qi Wei, Fei Qiao, Xinjun Liu, Huazhong Yang   +7 more
doaj   +1 more source

Reconstructing clean speech from noisy MFCC vectors

, 2009
The aim of this work is to reconstruct clean speech solely from a stream of noise-contaminated MFCC vectors, as may be encountered in distributed speech recognition systems.
Darch, Jonathan, Milner, Ben, Almajai, Ibrahim   +2 more
core   +6 more sources

GAMMATONE AND MFCC FEATURES IN SPEAKER RECOGNITION

, 2014
The feature analysis component of an Automated Speaker Recognition (ASR) system plays a crucial role in the overall performance of the system. There are many feature extraction techniques available, but ultimately we want to maximize the performance of ...
Burgos, Wison
openaire   +2 more sources

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