Sn-Co-Ni alloys of 25 different compositions have been prepared and equilibrated at 250°C. A new ternary (Ni, Co)Sn 4 phase is found, and all the binary compounds have very significant mutual solubilities between Co and Ni. According to the ternary equilibrium phase data and the constituent binary phase diagrams, the 250°C isothermal section of the Sn-Co-Ni system is proposed. In the Sn-CoSn-Ni 3 Sn 4 corner, there are nine two-phase regions and five tie-triangles. Interfacial reactions between molten Sn and Ni-1.0 at. pct Co, Ni-3.0 at. pct Co, Ni-5.0 at. pct Co, Ni-20.0 at. pct Co, and Ni-40.0 at. pct Co substrates are examined. It has been found that the interfacial reactions in Sn/Ni-1.0 at. pct Co are similar to those in Sn/Ni couples, and only the Ni 3 Sn 4 phase is formed. The (Ni, Co)Sn 4 phase is formed in the couples prepared with Ni-3.0 at. pct Co, Ni-5.0 at. pct Co, and Ni-20.0 at. pct Co substrates. It grows very fast and has a faceted and unique morphology.
The Gaussian Mixture Model -Universal Background Model (GMM-UBM) system is one of the predominant approaches for text-independent speaker verification, because both the target speaker model and the impostor model (UBM) have generalization ability to handle "unseen" acoustic patterns. However, since GMM-UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors' voices that are similar to the target speaker's voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the anti-model, while preserving the generalization ability of the GMM-UBM approach. This is achieved by adapting the UBM to a target speaker dependent anti-model based on a minimum verification squared-error criterion, rather than estimating the model from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NIST2001-SRE database show that DFA substantially improves the performance of the conventional GMM-UBM approach.1
Speaker verification based on the log-likelihood ratio (LLR) is essentially a task of modeling and testing two hypotheses: the null hypothesis and the alternative hypothesis. Since the alternative hypothesis involves unknown imposters, it is usually hard to characterize a priori. In this paper, we propose a framework to better characterize the alternative hypothesis with the goal of optimally separating client speakers from imposters. The proposed framework is built on either a weighted arithmetic combination or a weighted geometric combination of useful information extracted from a set of pre-trained anti-speaker models. The parameters associated with the combinations are then optimized using Minimum Verification Error training such that both the false acceptance probability and the false rejection probability are minimized. Our experiment results show that the proposed framework outperforms conventional LLR-based approaches.
The GMM-UBM system is the current state-of-the-art approach for text-independent speaker verification. The advantage of the approach is that both target speaker model and impostor model (UBM) have generalization ability to handle "unseen" acoustic patterns. However, since GMM-UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors' voices that are similar to the target speaker's voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the antimodel, while preserves the generalization ability of the GMM-UBM approach. This is done by adapting the UBM to a target-speakerdependent anti-model based on a minimum verification squarederror criterion, rather than estimating from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NIST2001-SRE database show that DFA substantially improves the performance of the conventional GMM-UBM approach.Index Terms-Discriminative feedback adaptation, loglikelihood ratio, minimum verification squared-error linear regression, speaker verification
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