Speaker verification using sequence discriminant support vector machines

This paper presents a text-independent speaker verification system using support vector machines (SVMs) with score-space kernels. Score-space kernels generalize Fisher kernels and are based on underlying generative models such as Gaussian mixture models (GMMs). This approach provides direct discrimination between whole sequences, in contrast with the frame-level approaches at the heart of most current systems. The resultant SVMs have a very high dimensionality since it is related to the number of parameters in the underlying generative model. To address problems that arise in the resultant optimization we introduce a technique called spherical normalization that preconditions the Hessian matrix. We have performed speaker verification experiments using the PolyVar database. The SVM system presented here reduces the relative error rates by 34% compared to a GMM likelihood ratio system

Speech and crosstalk detection in multichannel audio

The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges. For example, if each participant wears a microphone, speech from both the microphone's wearer (local speech) and from other participants (crosstalk) is received. The recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe two experiments related to the automatic classification of audio into these four classes. The first experiment attempted to optimize a set of acoustic features for use with a Gaussian mixture model (GMM) classifier. A large set of potential acoustic features were considered, some of which have been employed in previous studies. The best-performing features were found to be kurtosis, "fundamentalness," and cross-correlation metrics. The second experiment used these features to train an ergodic hidden Markov model classifier. Tests performed on a large corpus of recorded meetings show classification accuracies of up to 96%, and automatic speech recognition performance close to that obtained using ground truth segmentation

Risk of vaccine preventable diseases in UK migrants: A serosurvey and concordance analysis.

BACKGROUND: Vaccine preventable diseases (VPDs) such as measles and rubella cause significant morbidity and mortality globally every year. The World Health Organization (WHO), reported vaccine coverage for both measles and rubella to be 71 % in 2019, indicating an immunity gap. Migrants in the EU/EEA may be at high risk of VPDs due to under-immunisation and poor living conditions. However, there are limited data on VPD seroprotection rates amongst migrants living in the United Kingdom (UK). METHODS: We conducted an exploratory cross-sectional serosurvey amongst a sample of adult migrants living in Leicester, UK to: (a) determine seroprotection rates for measles, varicella zoster, and rubella in this group; (b) identify risk factors associated with seronegativity and, (c) understand if self-reported vaccine or diseases history is an effective measure of seroprotection. Participants gave a blood sample and completed a questionnaire asking basic demographic details and vaccine and disease history for the three VPDs. We summarised the data using median and interquartile range (IQR) for non-parametric continuous variables and count and percentage for categorical variables. We used logistic regression to establish predictors of seroprotection against these diseases. We examined the reliability of self-reported vaccination/disease history for prediction of seroprotection through a concordance analysis. RESULTS: 149 migrants were included in the analysis. Seroprotection rates were: varicella zoster 98 %, rubella 92.6 % and measles 89.3 %. Increasing age was associated with seroprotection (OR 1.07 95 % CI 1.01-1.13 for each year increase in age). Migrants from Africa and the Middle East (aOR 15.16 95 % CI 1.31 - 175.06) and South/East Asia and Pacific regions (aOR 15.43 95 %CI 2.38 - 100.00) are significantly more likely to be seroprotected against measles as compared to migrants from Europe and Central Asia. The proportions of migrants unsure about their vaccination and disease history combined were 53.0 % for measles; 57.7 % for rubella; 43.0 % for varicella. There was no agreement between self-reported vaccination/disease history and serostatus. CONCLUSION: Our findings suggest lower levels of seroprotection against measles in migrants living in Leicester, UK, with younger migrants and those from Europe and Central Asia more likely to lack seroprotection. A high proportion of surveyed migrants were unaware of their vaccination/disease history and self-reported vaccine/disease was a poor predictor of seroprotection against VPDs which is important for clinical decision-making regarding catch-up vaccination in this population. Our results, although derived from a small sample, suggest that there may be gaps in seroimmunity for certain VPDs in particular migrant populations. These findings should inform future qualitative studies investigating barriers to vaccine uptake in migrants and population-level seroprevalence studies aimed at determining individualised risk profiles based on demographic and migration factors

Automatic summarization of voicemail messages using lexical and prosodic features

This article presents trainable methods for extracting principal content words from voicemail messages. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words with each word described by a vector of lexical and prosodic features. We use an ROC-based algorithm, Parcel, to select input features (and classifiers). We have performed a series of objective and subjective evaluations using unseen data from two different speech recognition systems as well as human transcriptions of voicemail speech

Significant benefits of AIP testing and clinical screening in familial isolated and young-onset pituitary tumors

Context Germline mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene are responsible for a subset of familial isolated pituitary adenoma (FIPA) cases and sporadic pituitary neuroendocrine tumors (PitNETs). Objective To compare prospectively diagnosed AIP mutation-positive (AIPmut) PitNET patients with clinically presenting patients and to compare the clinical characteristics of AIPmut and AIPneg PitNET patients. Design 12-year prospective, observational study. Participants & Setting We studied probands and family members of FIPA kindreds and sporadic patients with disease onset ≤18 years or macroadenomas with onset ≤30 years (n = 1477). This was a collaborative study conducted at referral centers for pituitary diseases. Interventions & Outcome AIP testing and clinical screening for pituitary disease. Comparison of characteristics of prospectively diagnosed (n = 22) vs clinically presenting AIPmut PitNET patients (n = 145), and AIPmut (n = 167) vs AIPneg PitNET patients (n = 1310). Results Prospectively diagnosed AIPmut PitNET patients had smaller lesions with less suprasellar extension or cavernous sinus invasion and required fewer treatments with fewer operations and no radiotherapy compared with clinically presenting cases; there were fewer cases with active disease and hypopituitarism at last follow-up. When comparing AIPmut and AIPneg cases, AIPmut patients were more often males, younger, more often had GH excess, pituitary apoplexy, suprasellar extension, and more patients required multimodal therapy, including radiotherapy. AIPmut patients (n = 136) with GH excess were taller than AIPneg counterparts (n = 650). Conclusions Prospectively diagnosed AIPmut patients show better outcomes than clinically presenting cases, demonstrating the benefits of genetic and clinical screening. AIP-related pituitary disease has a wide spectrum ranging from aggressively growing lesions to stable or indolent disease course

European Language Grid : an overview

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented, by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 National Competence Centres (NCCs) and the European LT Council (LTC) for outreach and coordination purposes