This paper proposes a simple and e cient time domain technique to estimate an all-poll model on a mel-frequency axis (Mel-LPC). This method requires only two-fold computational cost as compared to conventional linear prediction analysis. The recognition performance of mel-cepstral parameters obtained by the Mel LPC analysis is compared with those of conventional LP mel-cepstra and the melfreque...

NEURO BASED APPROACH FOR SPEECH RECOGNITION BY USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS R.L.K. Venkateswarlu1 and R. Vasanthakumari2 1 Department of Information Technology, Sasi Institute of Technology and Engineering, Tadepalligudem, India, E-mail: [email protected]. 2 Perunthalaivar Kamarajar Arts College, Puducherry-605107, India, E-mail: [email protected]. This paper presents continu...

In this paper a method of text-independent speaker recognition using discrete vector quantization is presented. The identification experiments were performed in a closed set of 599 speakers and two various types of features were tested: cepstral mean subtraction coefficients and mel-frequency cepstral coefficients. The effect of the various codebook size on the speaker identification performanc...

This paper describes a novel approach to monitor a baby and it’s emotion and needs. Feature extraction methods like Magnitude Sum function, Pitch and Energy have been performed to classify the signal. These extraction techniques are proven to be more accurate than the conventional techniques. Although combinations of all three techniques have to be used to achieve 100% accuracy, the computation...

In this paper, we describe a brief overview of the speaker recognition techniques with their processing steps. Speaker recognition has many problems in feature extraction due to the robustness of the speech with noise. Gamma Tone Filter Bank and Wavelet Packet for the speaker recognition have the best performance over the Hidden Markov Model, Mel Frequency Cepstral Coefficient, Dynamic Time War...

Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers...

The process of converting an acoustic waveform into the text resembling the information, conveyed by the speaker is termed as speech recognition. Nowadays, normally Hidden Markov Model (HMM) based speech recognizer with Mel Frequency Cepstral Coefficient (MFCC) feature extraction is used. The proposed speech feature vector is generated by projecting an observed vector onto an Integrated Phoneme...

We introduce a set of speaker dependent features derived from the positions of vowels in Mel-Frequency Cepstral Coefficient (MFCC) space relative to a reference vowel. The MFCCs for a particular speaker are transformed using simple operations into features that can be used to classify vowels from a common reference point. Classification performance of vowels using Gaussian Mixture Models (GMMs)...

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is derived by the SVM incorporating the optimally weighted mel-frequency cepstral coefficient (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that optimal weights are differentl...

This study proposes using units smaller than words, such as phonemes and syllables, as base units for speech recognition. The system presented here was developed with a hierarchical recognition logic based on the production characteristics of phonemes in Brazilian Portuguese. Decisions are made by Support Vector Machine neural networks grouped to form Specialist Machines. The descriptors used w...