We propose harmonic-aligned frame mask for speech signals using non-stationary Gabor transform (NSGT).A frame mask operates on the transfer coefficients of a signal and consequently converts the signal into a counterpart signal.It depicts the difference between the two signals.In preceding studies, frame masks based on regular Gabor transform were applied to single-note instrumental sound analysis.This study extends the frame mask approach to speech signals.For voiced speech, the fundamental frequency is usually changing consecutively over time.We employ NSGT with pitch-dependent and therefore time-varying frequency resolution to attain harmonic alignment in the transform domain and hence yield harmonic-aligned frame masks for speech signals.We propose to apply the harmonic-aligned frame mask to content-dependent speaker comparison.Frame masks, computed from voiced signals of a same vowel but from different speakers, were utilized as similarity measures to compare and distinguish the speaker identities (SID).Results obtained with deep neural networks demonstrate that the proposed frame mask is valid in representing speaker characteristics and shows a potential for SID applications in limited data scenarios.

To post-process outputs of speech separation systems with harmonic enhancement, it is normally required to estimate the fundamental frequency. This paper evaluates the performance of a few representative robust pitch estimation algorithms on speech reconstructed from two-speaker mixture signals. The separation outputs obtained by two state-of-the-art single-channel separation algorithms are used for the evaluation. A recently proposed sparsity-based pitch estimation method is applied to the separated speech and a new pitch tracking algorithm is proposed. Experimental results show that on the separated speech the proposed method consistently surpasses the others with significantly low gross error rate, which is similar to the gross error rates of the other methods on clean speech.

Pitch estimation from acoustic signals is a fundamental problem in many areas of speech research. For noise-corrupted speech, reliable pitch estimation is difficult. This paper presents a study of pitch estimation in noisy speech based on robust temporal-spectral representation and sparse reconstruction. We propose to accumulate spectral peaks over consecutive time frames. Since harmonic structure of speech changes much more slowly than noise spectrum, spectral peaks related to pitch harmonics would stand out over the noise through the accumulation. Experimental results show that the accumulated peak spectrum is indeed a robust representation of pitch harmonics. Subsequently, the accumulated peak spectrum is expressed as a sparse linear combination of a large set of clean peak spectrum exemplars. Gaussian mixture density is used to model noise spectrum peaks. The weights of the linear combination are estimated so as to maximize the likelihood of the accumulated peak spectrum under sparsity constraint. Robust pitch estimation is done based on the sparse weights and the corresponding peak spectrum exemplars. The use of Gaussian mixture model leads to non-convexity of the objective function for sparse weight estimation. By approximation and reformulation, two convex optimization approaches are developed to estimate the weights. Extensive experimental studies are carried out to evaluate performance of the proposed pitch estimation algorithms on a wide variety of noise conditions. It is clearly shown that the proposed methods significantly and consistently outperform the conventional methods, particularly at very low signal-to-noise ratios (e.g., SNR <; -5 dB).

In this paper, we propose a confidence measure for the pitch estimation method presented in a parallel paper [1]. The confidence measure is derived based on a sparse representation of the speech harmonic structure. The measurement quantitatively reflects the harmonicity associated with an estimated pitch. It is used to indicate the reliability of the results. Histogram is employed to illustrate the distribution of the measurement obtained from speech signals at low signal-to-noise ratios. It is shown that with the confidence measure, correct results can be effectively identified. By using the confidence measure, an adaptive algorithm for pitch estimation is proposed. Reliable pitch values obtained from preceding estimations are identified and used to predict the local pitch range for subsequent estimations. Parameter of the estimation algorithm is dynamically adjusted according to the predicted pitch range. Experimental results show that with the adaptive algorithm, pitch estimation accuracy is noticeably improved.

In this paper, we present a transform-domain Wiener filtering approach for enhancing speech periodicity. The enhancement is performed on the linear prediction residual signal. Two sequential lapped frequency transforms are applied to the residual in a pitch-synchronous manner. The residual signal is effectively represented by two separate sets of transform coefficients that correspond to the periodic and aperiodic components, respectively. A Wiener filter operating on the transform coefficients is developed to restore periodicity and reduce noise. Different filter parameters are designed for the transform coefficients of the periodic and aperiodic components. A template-driven method is used to estimate the filter parameters for the periodic component. For the aperiodic components, the filter parameters are computed based on a local SNR for effective noise reduction. Experimental results confirm that the harmonic structure of the signal can be effectively restored with the proposed approach.