نتایج جستجو برای: mfcc

تعداد نتایج: 1901  

2011
N. Murali Krishna P. V. Lakshmi Y. Srinivas J. Sirisha Devi

Emotion recognition helps to recognize the internal expressions of the individuals from the speech database. In this paper, Dynamic time warping (DTW) technique is utilized to recognize speaker independent Emotion recognition based on 39 MFCC features. A large audio of around 960 samples of isolated words of five different emotions are collected and recorded at 20 to 300 KHz sampling frequency....

2015
H. B. Chauhan

The study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and t...

2014
Hajer Rahali Zied Hajaiej Noureddine Ellouze

In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC), based on gammachirp filterbank, Relative Spectral (RASTA) and Autoregressive Moving-Average (ARMA) filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Fr...

2017
Asma Mansour Zied Lachiri

Enhancing the performance of emotional speaker recognition process has witnessed an increasing interest in the last years. This paper highlights a methodology for speaker recognition under different emotional states based on the multiclass Support Vector Machine (SVM) classifier. We compare two feature extraction methods which are used to represent emotional speech utterances in order to obtain...

2011
Ravi Kumar

----------------------------------------------------------------------ABSTRACT------------------------------------------------------------------The objective approach has an advantage over the manual, which provides consistence measurement required for assessment of stuttered speech. The number of dimensions (multi dimension) plays a key role in objective assessment of stuttering. The purpose o...

2004
Rajesh M. Hegde Hema A. Murthy

Speakers are generally identified by using features derived from the Fourier transform magnitude. The Modified group delay feature(MODGDF) derived from the Fourier transform phase has been used effectively for speaker recognition in our previous efforts.Although the efficacy of the MODGDF as an alternative to the MFCC is yet to be established, it has been shown in our earlier work that composit...

2015
R. Christopher Praveen S. Suguna

Nowadays the electronic gadgets have been updated to store large amount of music information. It is necessary to have an efficient retrieval system to choose the required data. The important task in audio retrieval system is feature extraction. In the feature extraction stage, the feature which gives relevant information about music has to be extracted. In this paper, various Mel based feature ...

2004
Chih-Wen Weng Cheng-Yuan Lin Jyh-Shing Roger Jang

In the analysis of musical acoustics, we usually use the power spectrum to describe the difference between timbres from two music instruments. However, according to our experiments, the power spectrum cannot be used as effective features for erhu instrument identification. In this paper, we use MFCC (mel-scale frequency cepstral coefficients) as features for music instrument identification usin...

2011
Shweta Ghai Rohit Sinha

In this work, following our previous studies, we study and quantify the effect of pitch on LPCC and PLPC features and explore their efficacy for children’s mismatched ASR in comparison to MFCC. Our analysis shows that, unlike MFCC, LPCC feature has no major influence of pitch variations. On the other hand, similar to MFCC, though PLPC is also found to be significantly effected by pitch variatio...

2015
Mahaveer Chougala

this paper introduces a new method of extracting MFCC for speech recognition and it is compared with the conventional MFCC method. The new algorithm reduces the calculation steps by 53% compared to conventional method. Simulation result indicates the new method has a recognition accuracy of 92.93% only 1.5% less than the conventional MFCC method which is has accuracy of 94.43%. However, the num...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید