Annotating large lattices with the exact word error

The acoustic model in modern speech recognisers is trained discriminatively, for example with the minimum Bayes risk. This criterion is hard to compute exactly, so that it is normally approximated by a criterion that uses fixed alignments of lattice arcs. This approximation becomes particularly problematic with new types of acoustic models that require flexible alignments. It would be best to annotate lattices with the risk measure of interest, the exact word error. However, the algorithm for this uses finite-state automaton determinisation, which has exponential complexity and runs out of memory for large lattices. This paper introduces a novel method for determinising and minimising finite-state automata incrementally. Since it uses less memory, it can be applied to larger lattices.This work was supported by EPSRC Project EP/I006583/1 (Generative Kernels and Score Spaces for Classification of Speech) within the Global Uncertainties Programme and by a Google Research Award.This is the author accepted manuscript. The final version is available from ISCA via http://www.isca-speech.org/archive/interspeech_2015/i15_2625.htm

Automatically Grading Learners’ English Using a Gaussian Process

There is a high demand around the world for the learning of English as a second language. Correspondingly, there is a need to assess the proficiency level of learners both during their studies and for formal qualifications. A number of automatic methods have been proposed to help meet this demand with varying degrees of success. This paper considers the automatic assessment of spoken English proficiency, which is still a challenging problem. In this scenario, the grader should be able to accurately assess the learner’s ability level from spontaneous, prompted, speech, independent of L1 language and the quality of the audio recording. Automatic graders are potentially more consistent than humans. However, the validity of the predicted grade varies. This paper proposes an automatic grader based on a Gaussian process. The advantage of using a Gaussian process is that as well as predicting a grade, it provides a measure of the uncertainty of its prediction. The uncertainty measure is sufficiently accurate to decide which automatic grades should be re-graded by humans. It can also be used to determine which candidates are hard to grade for humans and therefore need expert grading. Performance of the automatic grader is shown to be close to human graders on real candidate entries. Interpolation of human and GP grades further boosts performance.This work was supported by Cambridge English, University of Cambridge.This is the author accepted manuscript. The final version is available from ISCA via http://www.isca-speech.org/archive/slate_2015/sl15_007.htm

Improving multiple-crowd-sourced transcriptions using a speech recogniser

This paper introduces a method to produce high-quality transcrip- tions of speech data from only two crowd-sourced transcriptions. These transcriptions, produced cheaply by people on the Internet, for example through Amazon Mechanical Turk, are often of low qual- ity. Often, multiple crowd-sourced transcriptions are combined to form one transcription of higher quality. However, the state of the art is to use essentially a form of majority voting, which requires at least three transcriptions for each utterance. This paper shows how to refine this approach to work with only two transcriptions. It then introduces a method that uses a speech recogniser (bootstrapped on a simple combination scheme) to combine transcriptions. When only two crowd-sourced transcriptions are available, on a noisy data set this improves the word error rate to gold-standard transcriptions by 21 % relative.This paper reports on research supported by Cambridge English, University of Cambridge.This is the accepted manuscript of a paper that will be published in the Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. It is currently under an infinite embargo

Off-topic response detection for spontaneous spoken English assessment

Automatic spoken language assessment systems are becoming increasingly important to meet the demand for English second language learning. This is a challenging task due to the high error rates of, even state-of-the-art, non-native speech recognition. Consequently current systems primarily assess fluency and pronunciation. However, content assessment is essential for full automation. As a first stage it is important to judge whether the speaker responds on topic to test questions designed to elicit spontaneous speech. Standard approaches to off-topic response detection assess similarity between the response and question based on bag-of-words representations. An alternative framework based on Recurrent Neural Network Language Models (RNNLM) is proposed in this paper. The RNNLM is adapted to the topic of each test question. It learns to associate example responses to questions with points in a topic space constructed using these example responses. Classification is done by ranking the topic-conditional posterior probabilities of a response. The RNNLMs associate a broad range of responses with each topic, incorporate sequence information and scale better with additional training data, unlike standard methods. On experiments conducted on data from the Business Language Testing Service (BULATS) this approach outperforms standard approaches

Towards automatic assessment of spontaneous spoken English

With increasing global demand for learning English as a second language, there has been considerable interest in methods of automatic assessment of spoken language proficiency for use in interactive electronic learning tools as well as for grading candidates for formal qualifications. This paper presents an automatic system to address the assessment of spontaneous spoken language. Prompts or questions requiring spontaneous speech responses elicit more natural speech which better reflects a learner’s proficiency level than read speech. In addition to the challenges of highly variable non-native, learner, speech and noisy real-world recording conditions, this requires any automatic system to handle disfluent, non-grammatical, spontaneous speech with the underlying text unknown. To handle these, a strong deep learning based speech recognition system is applied in combination with a Gaussian Process (GP) grader. A range of features derived from the audio using the recognition hypothesis are investigated for their efficacy in the automatic grader. The proposed system is shown to predict grades at a similar level to the original examiner graders on real candidate entries. Interpolation with the examiner grades further boosts performance. The ability to reject poorly estimated grades is also important and measures are proposed to evaluate the performance of rejection schemes. The GP variance is used to decide which automatic grades should be rejected. Back-off to an expert grader for the least confident grades gives gains.Cambridge Assessment Englis

Tumor markers in breast cancer - European Group on Tumor Markers recommendations

Recommendations are presented for the routine clinical use of serum and tissue-based markers in the diagnosis and management of patients with breast cancer. Their low sensitivity and specificity preclude the use of serum markers such as the MUC-1 mucin glycoproteins ( CA 15.3, BR 27.29) and carcinoembryonic antigen in the diagnosis of early breast cancer. However, serial measurement of these markers can result in the early detection of recurrent disease as well as indicate the efficacy of therapy. Of the tissue-based markers, measurement of estrogen and progesterone receptors is mandatory in the selection of patients for treatment with hormone therapy, while HER-2 is essential in selecting patients with advanced breast cancer for treatment with Herceptin ( trastuzumab). Urokinase plasminogen activator and plasminogen activator inhibitor 1 are recently validated prognostic markers for lymph node-negative breast cancer patients and thus may be of value in selecting node-negative patients that do not require adjuvant chemotherapy. Copyright (C) 2005 S. Karger AG, Basel

Late cardiac events after childhood cancer: Methodological aspects of the pan-european study pancaresurfup

Background and Aim Childhood cancer survivors are at high risk of long-Termadverse effects of cancer and its treatment, including cardiac events. The pan-European PanCareSurFup study determined the incidence and risk factors for cardiac events among childhood cancer survivors. The aim of this article is to describe the methodology of the cardiac cohort and nested case-control study within PanCareSurFup. Methods Eight data providers in Europe participating in PanCareSurFup identified and validated symptomatic cardiac events in their cohorts of childhood cancer survivors. Data onsymptomatic heart failure, ischemia, pericarditis, valvular disease and arrhythmia were collected and graded according to the Criteria for Adverse Events. Detailed treatment data, data on potential confounders, lifestyle related risk factors and general health problems were collected. Results The PanCareSurFup cardiac cohort consisted of 59,915 5-year childhood cancer survivors with malignancies diagnosed between 1940 and 2009 and classified according to the International Classification of Childhood Cancer 3. Different strategies were used to identify cardiac events such as record linkage to population/ hospital or regional based databases, and patient-And general practitioner-based questionnaires. Conclusion The cardiac study of the European collaborative research project PanCareSurFup will provide the largest cohort of 5-year childhood cancer survivors with systematically ascertained and validated data on symptomatic cardiac events. The result of this study can provide information to minimize the burden of cardiac events in childhood cancer survivors by tailoring the follow-up of childhood cancer survivors at high risk of cardiac adverse events, transferring this knowledge into evidence-based clinical practice guidelines and providing a platformfor future research studies in childhood cancer patients. © 2016 Feijen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Learning physical examination skills outside timetabled training sessions: what happens and why?

Lack of published studies on students’ practice behaviour of physical examination skills outside timetabled training sessions inspired this study into what activities medical students undertake to improve their skills and factors influencing this. Six focus groups of a total of 52 students from Years 1–3 using a pre-established interview guide. Interviews were recorded, transcribed and analyzed using qualitative methods. The interview guide was based on questionnaire results; overall response rate for Years 1–3 was 90% (n = 875). Students report a variety of activities to improve their physical examination skills. On average, students devote 20% of self-study time to skill training with Year 1 students practising significantly more than Year 3 students. Practice patterns shift from just-in-time learning to a longitudinal selfdirected approach. Factors influencing this change are assessment methods and simulated/real patients. Learning resources used include textbooks, examination guidelines, scientific articles, the Internet, videos/DVDs and scoring forms from previous OSCEs. Practising skills on fellow students happens at university rooms or at home. Also family and friends were mentioned to help. Simulated/real patients stimulated students to practise of physical examination skills, initially causing confusion and anxiety about skill performance but leading to increased feelings of competence. Difficult or enjoyable skills stimulate students to practise. The strategies students adopt to master physical examination skills outside timetabled training sessions are self-directed. OSCE assessment does have influence, but learning takes place also when there is no upcoming assessment. Simulated and real patients provide strong incentives to work on skills. Early patient contacts make students feel more prepared for clinical practice

Search for Charged Higgs Bosons in e+e- Collisions at \sqrt{s} = 189 GeV

A search for pair-produced charged Higgs bosons is performed with the L3 detector at LEP using data collected at a centre-of-mass energy of 188.6 GeV, corresponding to an integrated luminosity of 176.4 pb^-1. Higgs decays into a charm and a strange quark or into a tau lepton and its associated neutrino are considered. The observed events are consistent with the expectations from Standard Model background processes. A lower limit of 65.5 GeV on the charged Higgs mass is derived at 95 % confidence level, independent of the decay branching ratio Br(H^{+/-} -> tau nu)