In this paper, a recently proposed high-resolution Sinusoidal Model, dubbed the extended adaptive Quasi-Harmonic Model (eaQHM), is applied on modeling unvoiced speech sounds. Unvoiced speech sounds are parts of speech that are highly non-stationary in the time-frequency plane. Standard sinusoidal models fail to model them accurately and efficiently, thus introducing artefacts, while the reconstructed signals do not attain the quality and naturalness of the originals. Motivated by recently proposed non-stationary transforms, such as the Fan-Chirp Transform (FChT), eaQHM is tested to confront these effects and it is shown that highly accurate, artefact-free representations of unvoiced sounds are possible using the non-stationary properties of the model. Experiments on databases of unvoiced sounds show that, on average, eaQHM improves the Signal to Reconstruction Error Ratio (SRER) obtained by the standard Sinusoidal Model (SM) by 93%. Moreover, modeling superiority is also supported via informal listening tests with two other models, namely the SM and the well-known STRAIGHT method.

We propose a fast speech analysis method which simultaneously performs high-resolution voiced/unvoiced detection (VUD) and accurate estimation of glottal closure and glottal opening instants (GCIs and GOIs, respectively). The proposed algorithm exploits the structure of the glottal flow derivative in order to estimate GCIs and GOIs only in voiced speech using simple time-domain criteria. We compare our method with well-known GCI/GOI methods, namely, the dynamic programming projected phase-slope algorithm (DYPSA), the yet another GCI/GOI algorithm (YAGA) and the speech event detection using the residual excitation and a mean-based signal (SEDREAMS). Furthermore, we examine the performance of the aforementioned methods when combined with state-of-the-art VUD algorithms, namely, the robust algorithm for pitch tracking (RAPT) and the summation of residual harmonics (SRH). Experiments conducted on the APLAWD and SAM databases show that the proposed algorithm outperforms the state-of-the-art combinations of VUD and GCI/GOI algorithms with respect to almost all evaluation criteria for clean speech. Experiments on speech contaminated with several noise types (white Gaussian, babble, and car-interior) are also presented and discussed. The proposed algorithm outperforms the state-of-the-art combinations in most evaluation criteria for signal-to-noise ratio greater than 10 dB.

In this paper, a simple method for pitch-scale modifications of speech based on a recently suggested model for AM-FM decomposition of speech signals, is presented. This model is referred to as the adaptive Harmonic Model (aHM). The aHM models speech as a sum of harmonically related sinusoids that can adapt to the local characteristics of the signal. It was shown that this model provides high quality reconstruction of speech and thus, it can also provide high quality pitch-scale modifications. For the latter, the amplitude envelope is estimated using the Discrete All-Pole (DAP) method, and the phase envelope estimation is performed by utilizing the concept of relative phase. Formal listening tests on a database of several languages show that the synthetic pitch-scaled waveforms are natural and free of some common artefacts encountered in other state-of-the-art models, such as HNM and STRAIGHT.

Recent advances in speech analysis have shown that voiced speech can be very well represented using quasi-harmonic frequency tracks and local parameter adaptivity to the underlying signal. In this paper, we revisit the quasi-harmonicity approach through the extended adaptive Quasi-Harmonic Model - eaQHM, and we show that the application of a continuous f0estimation method plus an adaptivity scheme can yield high resolution quasi-harmonic analysis and perceptually indistinguishable resynthesized speech. This method assumes an initial harmonic model which successively converges to quasi-harmonicity. Formal listening tests showed that eaQHM is robust against f0estimation artefacts and can provide a higher quality in resynthesizing speech, compared to a recently developed model, called the adaptive Harmonic Model (aHM), and the standard Sinusoidal Model (SM).

In this paper, a simple method for time-scale modifications of speech based on a recently suggested model for AM-FM decomposition of speech signals, is presented. This model is referred to as the adaptive Harmonic Model (aHM). A full-band speech analysis/synthesis system based on the aHM representation is built, without the necessity of separating a deterministic and/or a stochastic component from the speech signal. The aHM models speech as a sum of harmonically related sinusoids that can adapt to the local characteristics of the signal and provide accurate instantaneous amplitude, frequency, and phase trajectories. Because of the high quality representation and reconstruction of speech, aHM can provide high quality time-scale modifications. Informal listenings show that the synthetic time-scaled waveforms are natural and free of some common artifacts encountered in other state-of-the-art models, such as “metallic quality”, chorusing, or musical noise.

In this paper, we present an extension of a recently developed AM-FM decomposition algorithm, which will be referred to as the extended adaptive Quasi-Harmonic Model (eaQHM). It was previously shown that the adaptive Quasi-Harmonic Model (aQHM) [1] is an efficient AM-FM decomposition algorithm with applications in speech analysis. In this paper, we show that a simple extension of the aQHM algorithm to include not only frequency but also amplitude adaptation results in higher performance in terms of Signal-to-Reconstruction-Error Ratio (SRER). To support our hypothesis, eaQHM is tested both on synthetic signals and on a subset of the ARCTIC database of speech. Overall, compared with aQHM, eaQHM improves the SRER by more than 2 dB, on average.

This paper presents and evaluates an inverse filtering technique of the speech signal which is based on the Stabilized Weighted Lin ear Prediction (SWLP) of speech. SWLP emphasizes the speech samples that fit the underlying speech production model well, by imposing temporal weighting of the square of the residual signal. The performance of SWLP is compared to the conventional Linear Prediction based inverse filtering techniques, such as the Autocorrelation and Closed Phase Covariance method. All the inverse filtering approaches are evaluated on a database of speech signals generated by a physical model of the voice production system. Results show that the estimated glottal flows using SWLP are closer to the original glottal flow than those estimated by the Autocorrelation approach, while its performance is comparable to the Closed Phase Covariance approach.