[PDF] Top 20 Noise-Robust Speech Features Based on Cepstral Time Coefficients
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Noise-Robust Speech Features Based on Cepstral Time Coefficients
... the cepstral mean subtraction (CMS) [3], cepstral variance normalization (CVN) [4], and histogram normalization (HEQ) [5] can lead to significant performance improvement in recognition accuracy in noisy ... See full document
8
VDCNN based Noise Robust Speech Recognition with Combination of GMM and MFCC Features
... The speech recognition is the mechanism to interpret the meaningful description of the input speech signal, which can be used for further ...The speech recognition models are utilized for ... See full document
9
Review on Computer Control with Voice Command (MFCC) using Ad-hoc Network
... traditional features in recognition of speech under stress and formulates new features which are shown to improve stressed speech ...formulating robust features which are less ... See full document
6
SEARCHING OF SPEECH QUERIES IN AN AUDIO DATABASE USING MEL FREQUENCY CEPSTRAL COEFFICIENTS AND GAUSSIAN POSTERIORGRAMS BASED FEATURES
... for time stamp. The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic ... See full document
6
Improving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
... of speech emotion recognition systems, Chauhan et ...emotional speech datasets which includes eight emotions anger, compassion, disgust, fear, happy, neutral, sarcastic and ...prosodic features such ... See full document
12
Accent Recognition for Malayalam Speech Signals
... spectrum features of each speech segment based on structured ...mel-frequency Cepstral Coefficients (MFCCs) features were used for speaker accent ...system based on accent ... See full document
7
Intra-frame cepstral sub-band weighting and histogram equalization for noise-robust speech recognition
... of cepstral frame-based processing to compensate for the noise effect to achieve better recognition ...each cepstral channel is processed by histogram equalization (HEQ), a significant ... See full document
18
An Approach To Feature Selection Algorithm Based On Ant Colony Optimization For Human Emotion Recognition Using Speech
... a speech signal like energy, pitch, Mel Frequency Cepstral Coefficient (MFCC) are important in finding out the state of a ...the speech signal is taken as the input and by means of MFCC feature ... See full document
7
Automatic Recognition of Machinery Noise in the Working Environment
... in speech or speaker recognition systems is the MFCC ...The time constant of features extraction is ...the noise generated by the observed machinery is less dynamic than speech; ... See full document
11
A New Robust Resonance Based Wavelet Decomposition Cepstral Features for Phoneme Recoszgnition
... Decomposition Cepstral Coefficients (RWDCC): Speech signal is composed of phonemes with different acoustic characteristics, for example some phonemes have high resonance (oscillatory) behavior like ... See full document
8
A Review on Neural Network based Noise Robust Speech Recognition Methods
... 1 time frame of a speech waveform 15 Mel Frequency Cepstral Coefficients are used as an input to the ...One time frame corresponds to the one column of phoneme ...In Speech ... See full document
7
Spectral Reconstruction and Noise Model Estimation Based on a Masking Model for Noise Robust Speech Recognition
... convolutive noise. Third, instead of using GMMs as prior speech models, the proposed algorithms could be extended to exploit the HMMs used by the recogniser as this would provide with additional temporal ... See full document
31
Speech to Text Conversion Using Discrete Hidden Markov Model
... years, Speech Recognition has the great development in the automation ...Automatic Speech Recognition (ASR) to facilitate an interaction between human and the electronic ...a robust speech ... See full document
8
Speaker recognition with hybrid features from a deep belief network
... For speaker recognition task, a first attempt on the use of RBMs has been reported by [6]. They use a single RBM training and apply the model to a speaker verification task. They model pairs of i-vectors using Gaussian- ... See full document
12
Human Breathing Classification Using Electromyography Signal with Features Based on Mel-Frequency Cepstral Coefficients
... signal based on ...is based on measurements of breathing air ...Mel-Frequency Cepstral Coefficients (MFCC) in providing the features for EMG ... See full document
8
Improving Speaker Identification Performance by Combining Vocal Tract Features
... TIMIT speech database under fusion of model scores and addition of features ...the features which outperforms the result obtained by adding the complementary ... See full document
7
Isolated English alphabet speech recognition using wavelet cepstral coefficients and neural network
... Next I would like to thank my lab mates for their advices and insights over the past two years of my stay at UTM. For this, special credits go to Khalid for introducing me to Latex. Without him introducing me to Latex I ... See full document
31
Effective News Video Classification Based On Audio Content: A Multiple Instance Learning Approach
... videos based on audio content using Multiple Instance Learning (MIL) ...and features have been extracted from each instances. Features of the instances of the same audio files are grouped together ... See full document
5
A Review: Person Recognition Based on Humming
... from time domain to frequency domain. FFT reduces computation time required to compute a DFT and improve ...into time domain using Discrete Cosine Transform ... See full document
5
Speaker identification - prototype development and performance
... The inverse Fourier transform separates the quickly varying and slowly varying parts from the log of the spectrum. The cepstrum basically decomposes the signal into the source and filter characteristics and can be ... See full document
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