5 research outputs found
Quality Measures for Speaker Verification with Short Utterances
The performances of the automatic speaker verification (ASV) systems degrade
due to the reduction in the amount of speech used for enrollment and
verification. Combining multiple systems based on different features and
classifiers considerably reduces speaker verification error rate with short
utterances. This work attempts to incorporate supplementary information during
the system combination process. We use quality of the estimated model
parameters as supplementary information. We introduce a class of novel quality
measures formulated using the zero-order sufficient statistics used during the
i-vector extraction process. We have used the proposed quality measures as side
information for combining ASV systems based on Gaussian mixture model-universal
background model (GMM-UBM) and i-vector. The proposed methods demonstrate
considerable improvement in speaker recognition performance on NIST SRE
corpora, especially in short duration conditions. We have also observed
improvement over existing systems based on different duration-based quality
measures.Comment: Accepted for publication in Digital Signal Processing: A Review
Journa
Quality Measures for Speaker Verification with Short Utterances
International audienceThe performances of the automatic speaker verification (ASV) systems degrade due to the reduction in amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as a supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model-universal background model (GMM-UBM) and i-vector. Considerable improvement is found in performance metrics by the proposed system on NIST SRE corpora in short duration conditions. We have observed improvement over state-of-the-art i-vector system
Time-varying autoregressions for speaker verification in reverberant conditions
In poor room acoustics conditions, speech signals received by a microphone might become corrupted by the signals’ delayed versions that are reflected from the room surfaces (e.g. wall, floor). This phenomenon, reverberation, drops the accuracy of automatic speaker verification systems by causing mismatch between the training and testing. Since reverberation causes temporal smearing to the signal, one way to tackle its effects is to study robust feature extraction, particularly based on long-time temporal feature extraction. This approach has been adopted previously in the form of 2-dimensional autoregressive (2DAR) feature extraction scheme by using frequency domain linear prediction (FDLP). In 2DAR, FDLP processing is followed by time domain linear prediction (TDLP). In the current study, we propose modifying the latter part of the 2DAR feature extraction scheme by replacing TDLP with time-varying linear prediction (TVLP) to add an extra layer of temporal processing. Our speaker verification experiments using the proposed features with the text-dependent RedDots corpus show small but consistent improvements in clean and reverberant conditions (up to 6.5%) over the 2DAR features and large improvements over the MFCC features in reverberant conditions (up to 46.5%).Peer reviewe
Social work with airports passengers
Social work at the airport is in to offer to passengers social services. The main
methodological position is that people are under stress, which characterized by a
particular set of characteristics in appearance and behavior. In such circumstances
passenger attracts in his actions some attention. Only person whom he trusts can help him
with the documents or psychologically