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This paper introduces the use of two new features for speaker identification, Residual Phase Cepstrum Coefficients (RPCC) and Glottal Flow Cepstrum Coefficients (GLFCC), to capture speaker-specific characteristics from their vocal excitation patterns. Results on a cross-lingual speaker identification task taken from the NIST 2004 SRE demonstrate that these RPCC and GLFCC features are significantly...
The article presents the development of a speaker identification system as one part of the multimodal interface for the HBB-NEXT project. A short introduction to a speaker identification problem in the context of HBB-NEXT project is given. Then we focus on the design, optimization and method selection process in order to realize a real time, text independent speaker identification application, namely:...
In this paper, various feature extraction techniques for text independent speaker identification such as Mel-frequency cepstral coefficients(MFCC), Modified Mel-frequency cepstral coefficients(MMFCC), Bark frequency cepstral coefficients(BFCC), Revised Perceptual liner prediction (RPLP) and linear predictive coefficient cepstrum (LPCC) are implemented and the comparison is done based on performance...
We present a new web-based application designed for human computer interface that currently supports speaker identification module. It is based on Java EE and Spring Framework and is designed to be invoked by users through their Internet browsers. Due to a flexible design various feature extraction methods, signal processing and classification algorithms can be easily implemented and used in different...
Mel Frequency Cepstral Coefficients (MFCC) are widely used in speech recognition and speaker identification. MFCC features are usually pre-processed before being used for recognition. One of these pre-processing is creating delta and delta-delta coefficients and append them to MFCC to create feature vector. Another pre-processing is coefficients mean normalization. In this paper, the effect of these...
This paper introduces the use of a new method of feature extraction based on frequency-time analysis approach for text-independent speaker identification. The impetus for this new feature extraction technique comes from the filter bank summation method of STFT using Nyquist filter bank. The focus of this work is on applications which yield higher identification accuracy without increasing the computational...
This paper introduces the use of a new method of feature extraction for robust text-independent speaker identification. The focus of this work is on applications which yield higher identification accuracy without increasing the computational effort. The impetus for this new feature extraction technique comes from a new transformation which is based on the Nyquist filter bank. We have proposed this...
The objective of this work is to demonstrate the significant speaker information present in the subband energies of the Linear Prediction (LP) residual. The LP residual mostly contains the excitation source information. The subband energies extracted using the mel filterbank followed by cepstral analysis provides a compact representation. The resulting cepstral values are termed as Residual-mel Frequency...
The performance of speaker identification systems has improved due to recent advances in speech processing techniques but there is still need of improvement in term of text-independent speaker identification and suitable modelling techniques for voice feature vectors. It becomes difficult for person to recognize a voice when an uncontrollable noise adds in to it. In this paper, feature vectors from...
In recent years, the field of automatic speaker identification has begun to exploit high-level sources of speaker-discriminative information, in addition to traditional models of spectral shape. These sources include pronunciation models, prosodic dynamics, pitch, pause, and duration features, phone streams, and conversational interaction. As part of this broader thrust, we explore a new frame-level...
The main objective of this paper is to explore the effectiveness of feature selection for performing composite speaker identification/verification. We propose features such as line spectral frequency (LSF), differential line spectral frequency (DLSF), mel frequency cepstral coefficients (MFCC), discrete cosine transform cepstrum (DCTC), perceptual linear predictive cepstrum (PLP) and mel frequency...
State-of-the-art Speaker Identification (SI) systems use Gaussian Mixture Models (GMM) for modeling speakerspsila data. Using GMM, a speaker can be identified accurately even from a large number of speakers, when model complexity is large. However, lower ordered speaker model using GMM show poor accuracy as lesser number of Gaussian are involved. In SI context, not much attention have been paid towards...
This paper presents a robust speaker identification approach basing on kernel principle component analysis (KPCA) and probabilistic neural network (PNN). KPCA is exploited to reduce the dimension of input vector and to denoise speech signal by extracting the nonlinear principle components of the feature vector. The extracted principle components are utilized as the input feature vector of the classifier...
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