This paper presents a method for interpolation of lost speech segments. The interpolation method can be used for packet loss concealment in voice communication over mobile phones, for voice over IP or for restoration of lost segments in speech recordings. The interpolation method employs a combination of a linear prediction (LP) model of the spectral envelope and a harmonic noise model (HNM) of the excitation of speech. The speech interpolation problem is transformed to the modeling and interpolation of the trajectories of LP parameters and the amplitude, phase and harmonicity of HNM tracks of speech excitation. In particular, the interpolation of harmonicity results in a smooth transition from voiced to unvoiced speech and vice versa. Crucially, the proposed interpolation method does not suffer from the consequences of zero-excitation of conventional autoregressive (AR) interpolation. Different combinations of linear and autoregressive interpolation methods are evaluated for the estimation of the time-varying parameters of LP-HNM tracks. Furthermore, a post-processing codebook mapping, employed to enhance the interpolation of the spectral envelope of speech, results in improved output quality for longer length speech gaps. For different packet loss rates and patterns of distributions of missing speech gaps, the proposed interpolation methods are evaluated and compared with popular AR-based interpolation methods and the speech packet recovery method specified in the ITU G.711 standard, as a reference. The evaluation results show that the proposed methods substantially improve the restoration of formants and harmonic tracks and consistently results in significant performance gain and improved perceptual quality of speech.

This paper presents a method for interpolation of lost speech segments. The short-time spectral amplitude (STSA) of speech is modeled using a linear prediction (LP) model of the spectral envelop and a harmonic plus noise model (HNM) of the excitation. The restoration algorithm is based on interpolation of the parameters of LP-HNM models of speech from both side of the gap. A codebook mapping (CBM) technique is used to fit the interpolated parameters to a pre-trained speech model. Experiments show that the CBM module mitigates the artifacts that may result from interpolation of relatively long speech gaps. Evaluations demonstrate that the proposed interpolation method results in a superior quality in comparison to alternative restoration methods.

This paper presents a post-processing speech restoration module for enhancing the performance of conventional speech enhancement methods. The restoration module aims to retrieve parts of speech spectrum that may be lost to noise or suppressed when using conventional speech enhancement methods. The proposed restoration method utilizes a harmonic plus noise model (HNM) of speech to retrieve damaged speech structure. A modified HNM of speech is proposed where, instead of the conventional binary labeling of the signal in each subband as voiced or unvoiced, the concept of harmonicity is introduced which is more adaptable to the codebook mapping method used in the later stage of enhancement. To restore the lost or suppressed information, an HNM codebook mapping technique is proposed. The HNM codebook is trained on speaker-independent speech data. To reduce the sensitivity of the HNM codebook to speaker variability, a spectral energy normalization process is introduced. The proposed post-processing method is tested as an add-on module with several popular noise reduction methods. Evaluations of the performance gain obtained from the proposed post-processing are presented and compared to standard speech enhancement systems which show substantial improvement gains in perceptual quality

This paper presents a method for restoration of the missing bandwidth of narrowband speech signals. Speech is decomposed into a linear prediction (LP) model of the spectral envelop and a harmonic plus noise model (HNM) of speech excitation. The LP spectral envelope and HNM excitation parameters of the narrowband speech are extrapolated using codebooks trained on narrowband and wideband speech. A novel contribution of this paper is the introduction of a parametric measure of the harmonicity of excitation in harmonically-spaced sub-bands. The wideband LSF parameters and the degree of harmonicity of missing excitation are estimated from those of the narrowband speech via codebook mapping. The method is successful in restoring the harmonicity of speech and converts telephone quality speech to perceptually high quality wideband speech.

This paper presents a time-frequency estimator for enhancement of noisy speech in the DFT domain. The time-varying trajectories of the DFT of speech and noise in each channel are modeled by low order autoregressive processes incorporated in the state equation of Kalman filters. The parameters of the Kalman filters are estimated recursively from the signal and noise in DFT channels. The issue of convergence of the Kalman filters to noise statistics during the noise-dominated periods is addressed and a method is incorporated for restarting of Kalman filters after long periods of noise-dominated activity in each DFT channel. The performance of the proposed method is compared with cases where the noise trajectories are not explicitly modeled. Evaluations show that the proposed method results in substantial improvement in perceived quality of speech.

Most noisy speech enhancement methods result in partial suppression and distortion of speech spectrum. At instances when the local signal-to-noise ratio at a frequency band is very low speech partials are often obliterated. In this paper a method for enhancement and restoration of noisy speech based on a harmonic-noise model (HNM) is introduced. A HNM imposes a temporal-spectral structure that may reduce processing artifacts. The restoration process is enhanced through incorporation of a prior HNM of clean speech stored in a pre-trained codebook. The restored speech is a SNRdependent combination of the de-noised observation and the speech obtained from weighted codebook mapping. The additional improvements of speech quality resulting from the proposed method in comparison to conventional and modern speech enhancement systems are evaluated. The results show that the proposed method improves the quality of noisy speech and restores much of the information lost to noise.

This paper presents a formant-tracking method for estimation of the time-varying trajectories of a linear prediction (LP) model of speech in noise. The main focus of this work is on the modelling of the non-stationary temporal trajectories of the formants of speech for improved LP model estimation in noise. The proposed approach provides a systematic framework for modelling the inter-frame correlation of speech parameters across successive frames, the intra-frame correlations are modelled by LP parameters. The formant-tracking LP model estimation is composed of two stages: (a) a pre-cleaning intra-frame spectral amplitude estimation stage where an initial estimate of the magnitude frequency response of the LP model of clean speech is obtained and (b) an inter-frame signal processing stage where formant classification and Kalman filters are combined to estimate the trajectory of formants. The effects of car and train noise on the observations and estimation of formants tracks are investigated. The average formant tracking errors at different signal to noise ratios (SNRs) are computed. The evaluation results demonstrate that after noise reduction and Kalman filtering the formant tracking errors are significantly reduced. 1.