In this paper, we propose a new method for the accurate estimation and tracking of formants
in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Conventional …

Current state-of-the-art automatic, continuous speech recognition systems have enjoyed
huge leaps in accuracy using speech features that assumes stationarity in the signals that …

Recent research on temporally weighted linear prediction shows that quasi closed phase
(QCP) analysis of speech signals provides better modeling of the vocal tract and the glottal …

We develop Bayesian learning algorithms for estimation of time-varying linear prediction
(TVLP) coefficients of speech. Estimation of TVLP coefficients is a naturally underdeter …

The extraction of the glottal volume velocity waveform from voiced speech is a well-known
example of a sparse signal recovery problem. Prior approaches have mostly used well …

In this paper, we propose a new method for accurate detection, estimation and tracking of
formants in speech signals using time-varying quasi-closed phase analysis (TVQCP). The …

We consider the joint estimation of time-varying linear prediction (TVLP) filter coefficients
and the excitation signal parameters for the analysis of long-term speech segments …

Compressed sensing is a new paradigm to explore the sparse nature of the signals.
Compressed sensing allows to acquire signals fundamentally below the uniform rate …

With the ever increasing abundance of multimedia data available on the Internet and crowd-
sourced datasets/repositories, there has been a renewed interest in machine learning …

Isolated word speech recognition for small vocabulary tasks has found great success with
Mel-frequency cepstral coefficients as the speech feature of choice. Voice-controlled …