Abstract—In most applications of sinusoidal models for speech signal, an amplitude spectral envelope is necessary. This envelope is not only assumed to fit the vocal tract filter response as accurately as possible, but it should also exhibit slow varying shapes across time. Indeed, time irregularities can generate artifacts in signal manipulations or increase improperly the features variance used in statistical models. In this letter, a simple technique is suggested to improve this time regularity. Considering that time regularity is characterized by slowly varying spectral shapes among successive frames, the basic idea is to smooth the frequency derivative of the envelope instead of its absolute value. Even though, this idea could be appli...
Abstract — The employment of nonlinear analysis techniques for automatic voice pathology detection s...
Spectral analysis of non-stationary signals is known to be a challenging task. Classical methods lik...
A common technique to deploy linear prediction to non-stationary signals is time segmentation and lo...
Speech modeling techniques used for analysis and synthesis usually rely on a source-filter represent...
Feature extraction of speech signals is typically performed in short-time frames by assuming that th...
Abstract — The efficient encoding of the harmonic spectral envelope is an essential task in parametr...
An analytic signal s(t) is modeled over a T second duration by a pole- zero model by considering its...
Speech signals contain a fairly rich time-evolving spectral content. Accurate analysis of this time-...
This paper presents methods for independently modifying the time and pitch scale of acoustic signals...
International audienceThis work proposes a new approach to estimating the speech spectral en...
Transient signals such as plosives in speech or Castanets in audio do not have a specific modulation...
The most commonly used time-frequency representation of the analysis in voice signal is spectrogram....
In automatic speech recognition, the signal is usually represented by a set of time sequences of spe...
very speech recognition system requires a signal representation that parametrically models the tempo...
Relative to the speech production and perception models, spectral envelopes play an important role i...
Abstract — The employment of nonlinear analysis techniques for automatic voice pathology detection s...
Spectral analysis of non-stationary signals is known to be a challenging task. Classical methods lik...
A common technique to deploy linear prediction to non-stationary signals is time segmentation and lo...
Speech modeling techniques used for analysis and synthesis usually rely on a source-filter represent...
Feature extraction of speech signals is typically performed in short-time frames by assuming that th...
Abstract — The efficient encoding of the harmonic spectral envelope is an essential task in parametr...
An analytic signal s(t) is modeled over a T second duration by a pole- zero model by considering its...
Speech signals contain a fairly rich time-evolving spectral content. Accurate analysis of this time-...
This paper presents methods for independently modifying the time and pitch scale of acoustic signals...
International audienceThis work proposes a new approach to estimating the speech spectral en...
Transient signals such as plosives in speech or Castanets in audio do not have a specific modulation...
The most commonly used time-frequency representation of the analysis in voice signal is spectrogram....
In automatic speech recognition, the signal is usually represented by a set of time sequences of spe...
very speech recognition system requires a signal representation that parametrically models the tempo...
Relative to the speech production and perception models, spectral envelopes play an important role i...
Abstract — The employment of nonlinear analysis techniques for automatic voice pathology detection s...
Spectral analysis of non-stationary signals is known to be a challenging task. Classical methods lik...
A common technique to deploy linear prediction to non-stationary signals is time segmentation and lo...