1,673 research outputs found
Speaker recognition using frequency filtered spectral energies
The spectral parameters that result from filtering the
frequency sequence of log mel-scaled filter-bank energies
with a simple first or second order FIR filter have proved
to be an efficient speech representation in terms of both
speech recognition rate and computational load. Recently,
the authors have shown that this frequency filtering can
approximately equalize the cepstrum variance enhancing
the oscillations of the spectral envelope curve that are
most effective for discrimination between speakers. Even
better speaker identification results than using melcepstrum
have been obtained on the TIMIT database,
especially when white noise was added. On the other
hand, the hybridization of both linear prediction and
filter-bank spectral analysis using either cepstral
transformation or the alternative frequency filtering has
been explored for speaker verification. The combination
of hybrid spectral analysis and frequency filtering, that
had shown to be able to outperform the conventional
techniques in clean and noisy word recognition, has yield
good text-dependent speaker verification results on the
new speaker-oriented telephone-line POLYCOST
database.Peer ReviewedPostprint (published version
Towards Personalized Synthesized Voices for Individuals with Vocal Disabilities: Voice Banking and Reconstruction
When individuals lose the ability to produce their own speech, due to degenerative diseases such as motor neurone disease (MND) or Parkinsonâs, they lose not only a functional means of communication but also a display of their individual and group identity. In order to build personalized synthetic voices, attempts have been made to capture the voice before it is lost, using a process known as voice banking. But, for some patients, the speech deterioration frequently coincides or quickly follows diagnosis. Using HMM-based speech synthesis, it is now possible to build personalized synthetic voices with minimal data recordings and even disordered speech. The power of this approach is that it is possible to use the patientâs recordings to adapt existing voice models pre-trained on many speakers. When the speech has begun to deteriorate, the adapted voice model can be further modified in order to compensate for the disordered characteristics found in the patientâs speech. The University of Edinburgh has initiated a project for voice banking and reconstruction based on this speech synthesis technology. At the current stage of the project, more than fifteen patients with MND have already been recorded and five of them have been delivered a reconstructed voice. In this paper, we present an overview of the project as well as subjective assessments of the reconstructed voices and feedback from patients and their families
Glottal Source Cepstrum Coefficients Applied to NIST SRE 2010
Through the present paper, a novel feature set for speaker recognition based on glottal estimate information is presented. An iterative algorithm is used to derive the vocal tract and glottal source estimations from speech signal. In order to test the importance of glottal source information in speaker characterization, the novel feature set has been tested in the 2010 NIST Speaker Recognition Evaluation (NIST SRE10). The proposed system uses glottal estimate parameter templates and classical cepstral information to build a model for each speaker involved in the recognition process. ALIZE [1] open-source software has been used to create the GMM models for both background and target speakers. Compared to using mel-frequency cepstrum coefficients (MFCC), the misclassification rate for the NIST SRE 2010 reduced from 29.43% to 27.15% when glottal source features are use
Digital signal processing algorithms for automatic voice recognition
The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms
Proposing a hybrid approach for emotion classification using audio and video data
Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have
become interested in emotion recognition and classification using different sources. A hybrid
approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this
approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification
accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%
A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition
In this paper, we propose a novel family of windowing technique to compute
Mel Frequency Cepstral Coefficient (MFCC) for automatic speaker recognition
from speech. The proposed method is based on fundamental property of discrete
time Fourier transform (DTFT) related to differentiation in frequency domain.
Classical windowing scheme such as Hamming window is modified to obtain
derivatives of discrete time Fourier transform coefficients. It has been
mathematically shown that the slope and phase of power spectrum are inherently
incorporated in newly computed cepstrum. Speaker recognition systems based on
our proposed family of window functions are shown to attain substantial and
consistent performance improvement over baseline single tapered Hamming window
as well as recently proposed multitaper windowing technique
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