Results 151 to 160 of about 3,813 (182)

Speech Recognition Combining MFCCs and Image Features

2016
Automatic speech recognition (ASR) task constitutes a well-known issue among fields like Natural Language Processing (NLP), Digital Signal Processing (DSP) and Machine Learning (ML). In this work, a robust supervised classification model is presented (MFCCs + autocor + SVM) for feature extraction of solo speech signals.
Stamatis Karlos, Nikos Fazakis, Katerina Karanikola, Sotiris B. Kotsiantis, Kyriakos N. Sgarbas   +4 more
openaire   +1 more source

Speech recognition using MFCC and DTW

2014 International Conference on Advances in Electrical Engineering (ICAEE), 2014
Speech recognition has wide range of applications in security systems, healthcare, telephony military, and equipment designed for handicapped. Speech is continuous varying signal. So, proper digital processing algorithm has to be selected for automatic speech recognition system. To obtain required information from the speech sample, features have to be
null Bhadragiri Jagan Mohan, null Ramesh Babu N.   +1 more
openaire   +1 more source

MFCCs and TEO-MFCCs for Stress Detection on Women Gender through Deep Learning Analysis

2023 9th International Conference on Computer and Communication Engineering (ICCCE), 2023
Nur Aishah Zainal, Ani Liza Asnawi, Ahmad Zamani Jusoh, Siti Noorjannah Ibrahim, Huda Adibah Mohd Ramli, Nor Fadhillah Mohamed Azmin   +5 more
openaire   +1 more source

Speech and Language Recognition using MFCC and DELTA-MFCC

International Journal of Engineering Trends and Technology, 2014
Samiksha Sharma, Anupam Shukla, Pankaj Mishra   +2 more
openaire   +1 more source

Integrating the energy information into MFCC

6th International Conference on Spoken Language Processing (ICSLP 2000), 2000
Fang Zheng 0001, Guoliang Zhang
openaire   +1 more source

Chinese Accents Identification with Modified MFCC

2012
It is well known that performance of Chinese speech recognition system fluctuates sharply with variation of accents. Hence it is feasible to identify accents before recognition. Based on our previous analysis [1], it is discovered that the first two formants are more sensitive to Chinese accents than others.
Qin Yan, Zhengjuan Zhou, Shan Li
openaire   +1 more source

Speech emotion recognition using MFCC-based entropy feature

Signal, Image and Video Processing, 2023
Siba Prasad Mishra, Deb Suman
exaly  

Retracted: Speaker recognition using MFCC, shifted MFCC with vector quantization and fuzzy

2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), 2015
Priyanka Bansal, Syed Akhtar Imam, Roma Bharti   +2 more
openaire   +1 more source

Retraction Notice: Speaker recognition using MFCC, shifted MFCC with vector quantization and fuzzy

2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), 2015
Priyanka Bansal, Syed Akhtar Imam, Roma Bharti   +2 more
openaire   +1 more source

Speaker normalization in the MFCC domain

6th International Conference on Spoken Language Processing (ICSLP 2000), 2000
openaire   +1 more source