Electromagnetic Articulography (EMA) technique is used to record the kinematics of different articulators while one speaks. EMA data often contains missing segments due to sensor failure. In this work, we propose a maximum a-posteriori (MAP) estimation with continuity constraint to recover the missing samples in the articulatory trajectories recorded using EMA. In this approach, we combine the benefits of statistical MAP estimation as well as the temporal continuity of the articulatory trajectories.

Multi-pitch tracks of speech mixtures are evaluated using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using Grating Compression Transform.

In subject-independent acoustic-to-articulatory inversion (SII), articulatory movements are estimated from the speech acoustics of a test speaker even though the test speaker may not be used for training. Several schemes for SII have been proposed. Below are the links for downloading the MATLAB codes for four best performing inversion schemes (IS), namely,

• IS2 without adaptation
• IS2 with adaptation
• IS3 without adaptation
• IS3 with adaptation

In this work, we propose an equality constrained Kalman smoother (ECKS) to estimate the missing samples in the EMA data. We incorporate the dynamics of the articulatory movement for missing samples estimation by considering the EMA data vector as the observations from a linear dynamical system. The proposed approach gives 41% reduction on the root mean square error of the estimates compared to the minimum mean square error estimator which does not utilize the dynamics of the articulatory movement.

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.

In this work, we propose a postprocessing scheme for correcting errors of any GCI detection algorithm. The error correction is formulated as an optimization problem such that the pitch contour from the corrected GCIs has the least high frequency components. The proposed error correction scheme is experimentally evaluated on speech corpus with simultaneous EGG recordings using three state-of-the-art GCI detection algorithms viz., DPI, ZFR, and SEDREAMS. It is found that the proposed error correction scheme improves the performance of the GCI detection in clean speech as well as noisy conditions at different SNRs.

Algorithms for extracting epochs or glottal closure instants (GCIs) from voiced speech typically fall into two categories: (i) ones which operate on linear prediction residual (LPR) and (ii) those which operate directly on the speech signal. While the former class of algorithms (such as YAGA and DPI) tend to be more accurate, the latter ones (such as ZFR and SEDREAMS) tend to be more noise-robust. In this paper, a temporal measure termed the cumulative impulse strength is proposed for locating the impulses in a quasi-periodic impulse-sequence embedded in noise. Subsequently, it is applied for detecting the GCIs from the inverted integrated LPR using a recursive algorithm. Experiments on two large corpora of speech with simultaneous electroglottographic recordings demonstrate that the proposed method is more robust to additive noise than the state-of-the-art algorithms, despite operating on the LPR.

Bird sound detection from real field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay (MWSG) filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared to its single window counterpart. It is known that bird sounds are highly structured in the time-frequency (T-F) plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision.

Accurate estimation of glottal closure instant (GCI) enables several pitch synchronous speech analysis such as prosody modifications glottal inverse filtering and study of pathological speech. We propose a probabilistic source-filter model (PSFM) for voiced speech where the source is modeled using the Bernoulli Gaussian distribution, which models the GCI locations and the all-pole filter coefficients are modeled using Gaussian distribution. The probability of GCIs at each speech sample is estimated using the Gibbs sampling. We propose a cost to estimate the exact GCI locations using the N-best dynamic programming. A key feature of the proposed PSFM is that it allows to include the second order statistics of the noise for estimating the GCI locations, thereby resulting in a noise robust GCI detection technique, although it has high computational complexity. Evaluation on APLAWD database shows the proposed algorithm performs at par with the state-of-the-art GCI detection method on clean speech. However, when evaluated in noisy conditions using five types of noises at six different signal to noise ratio (SNR) levels, we observe that the proposed method performs better than the best of the existing GCI detection scheme, particularly at low SNR condition indicating the noise robustness of the proposed method.