Speaker recognition performance degrades substantially in case of vocal effort mismatch (e.g. shouted vs. normal speech) between test and enrollment utterances. Such a mismatch is often encountered, for example, in forensic speaker recognition. This paper introduces a novel spectral mapping method which, when employed jointly with a statistical mapping technique, converts the Mel-frequency band energies of normal speech towards their counterparts in shouted speech. The aim is to obtain more robust performance in speaker recognition by tackling vocal effort mismatch between enrollment and test utterances. The processing is performed on the speech signal before feature extraction. The proposed approach was evaluated by testing the performance of a state-of-the-art i-vector-based speaker recognition system with and without applying the spectral mapping processing to the enrollment data. The results show that pre-processing with the proposed approach results in considerable improvement in correct identification rates.

Speaking style conversion is the technology of converting natural speech signals from one style to another. In this study, we focus on normal-to-Lombard conversion. This can be used, for example, to enhance the intelligibility of speech in noisy environments. We propose a parametric approach that uses a vocoder to extract speech features. These features are mapped using Bayesian GMMs from utterances spoken in normal style to the corresponding features of Lombard speech. Finally, the mapped features are converted to a Lombard speech waveform with the vocoder. Two vocoders were compared in the proposed normal-to-Lombard conversion: a recently developed glottal vocoder that decomposes speech into glottal flow excitation and vocal tract, and the widely used STRAIGHT vocoder. The conversion quality was evaluated in two subjective listening tests measuring subjective similarity and naturalness. The similarity test results show that the system is able to convert normal speech into Lombard speech for the two vocoders. However, the subjective naturalness of the converted Lombard speech was clearly better using the glottal vocoder in comparison to STRAIGHT.

In this paper, an extension of independent vector analysis (IVA), model-based IVA, is proposed for multichannel source separation. For obtaining better source models, we introduce a single-channel source separation method, and utilize the outputs as source variances in time-frequency-variant Gaussian source model. The demixing matrices are estimated in the same way as a state-of-the-art IVA method, auxiliary-function-based IVA (AuxIVA). Experimental evaluations show that the proposed approach is effective and improves the source separation performance of IVA. In addition, several post-filters aiming to realize multichannel Wiener filter (MWF) are investigated. This setup proves to further increase the performance of IVA. The presented method shows a potential to provide a general way to improve the separation performance from single-channel source separation to multichannel source separation.

Automatic speech recognition systems use noise compensation and acoustic model adaptation to increase robustness towards speaker and environmental variation. The current work focuses on noise compensation with bounded conditional mean imputation (BCMI). BCMI approaches are missing-data methods which operate on the assumption that noise-corrupted observations can be divided into reliable and unreliable components. BCMI methods substitute the unreliable components with a clean speech posterior distribution. The posterior means can be used as clean speech estimates and the posterior variances can be introduced in acoustic model likelihood calculation as observation uncertainties. In addition, we propose in the current work that similar uncertainties are introduced in acoustic model adaptation. Evaluation with speech data recorded in diverse public and car environments indicates that the proposed uncertainties improve adaptation performance. When uncertainties were used in acoustic model likelihood calculation and adaptation, the proposed imputation and adaptation system introduced 15%-84% relative error reductions to an uncompensated baseline system performance.