Speech register influences listeners’ word expectations

We utilized the N400 effect to investigate the influence of speech register on predictive language processing. Participants listened to long stretches (4 – 15 min) of naturalistic speech from different registers (dialogues, news broadcasts, and read-aloud books), totalling approximately 50,000 words, while the EEG signal was recorded. We estimated the surprisal of words in the speech materials with the aid of a statistical language model in such a manner that it reflected different predictive processing strategies; generic, register-specific, or recency-based. The N400 amplitude was best predicted with register-specific word surprisal, indicating that the statistics of the wider context (i.e., register) influences predictive language processing. Furthermore, adaptation to speech register cannot merely be explained by recency effects; instead, listeners adapt their word anticipations to the presented speech register

Development and Validation of a Risk Score for Chronic Kidney Disease in HIV Infection Using Prospective Cohort Data from the D:A:D Study

Ristola M. on työryhmien DAD Study Grp ; Royal Free Hosp Clin Cohort ; INSIGHT Study Grp ; SMART Study Grp ; ESPRIT Study Grp jäsen.Background Chronic kidney disease (CKD) is a major health issue for HIV-positive individuals, associated with increased morbidity and mortality. Development and implementation of a risk score model for CKD would allow comparison of the risks and benefits of adding potentially nephrotoxic antiretrovirals to a treatment regimen and would identify those at greatest risk of CKD. The aims of this study were to develop a simple, externally validated, and widely applicable long-term risk score model for CKD in HIV-positive individuals that can guide decision making in clinical practice. Methods and Findings A total of 17,954 HIV-positive individuals from the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study with >= 3 estimated glomerular filtration rate (eGFR) values after 1 January 2004 were included. Baseline was defined as the first eGFR > 60 ml/min/1.73 m2 after 1 January 2004; individuals with exposure to tenofovir, atazanavir, atazanavir/ritonavir, lopinavir/ritonavir, other boosted protease inhibitors before baseline were excluded. CKD was defined as confirmed (>3 mo apart) eGFR In the D:A:D study, 641 individuals developed CKD during 103,185 person-years of follow-up (PYFU; incidence 6.2/1,000 PYFU, 95% CI 5.7-6.7; median follow-up 6.1 y, range 0.3-9.1 y). Older age, intravenous drug use, hepatitis C coinfection, lower baseline eGFR, female gender, lower CD4 count nadir, hypertension, diabetes, and cardiovascular disease (CVD) predicted CKD. The adjusted incidence rate ratios of these nine categorical variables were scaled and summed to create the risk score. The median risk score at baseline was -2 (interquartile range -4 to 2). There was a 1: 393 chance of developing CKD in the next 5 y in the low risk group (risk score = 5, 505 events), respectively. Number needed to harm (NNTH) at 5 y when starting unboosted atazanavir or lopinavir/ritonavir among those with a low risk score was 1,702 (95% CI 1,166-3,367); NNTH was 202 (95% CI 159-278) and 21 (95% CI 19-23), respectively, for those with a medium and high risk score. NNTH was 739 (95% CI 506-1462), 88 (95% CI 69-121), and 9 (95% CI 8-10) for those with a low, medium, and high risk score, respectively, starting tenofovir, atazanavir/ritonavir, or another boosted protease inhibitor. The Royal Free Hospital Clinic Cohort included 2,548 individuals, of whom 94 individuals developed CKD (3.7%) during 18,376 PYFU (median follow-up 7.4 y, range 0.3-12.7 y). Of 2,013 individuals included from the SMART/ESPRIT control arms, 32 individuals developed CKD (1.6%) during 8,452 PYFU (median follow-up 4.1 y, range 0.6-8.1 y). External validation showed that the risk score predicted well in these cohorts. Limitations of this study included limited data on race and no information on proteinuria. Conclusions Both traditional and HIV-related risk factors were predictive of CKD. These factors were used to develop a risk score for CKD in HIV infection, externally validated, that has direct clinical relevance for patients and clinicians to weigh the benefits of certain antiretrovirals against the risk of CKD and to identify those at greatest risk of CKD.Peer reviewe

Improving out-of-coverage language modelling in a multimodal dialogue system using small training sets

For automatic speech recognition, the construction of an adequate language model may be difficult when only a limited amount of training text is available. Previous work has shown that in the case of small training sets statistical language models may outperform grammars on out-of-coverage utterances, while showing comparable performance on incoverage input. In this paper, we compare the performance of an automatic speech recognition system using a grammar and a statistical language model including garbage models in the case of very limited in-domain training data. The results show that a bigram language model and a grammar show similar performance, and that the inclusion of garbage models in statistical language models enhances their performance both on in-coverage and out-of-coverage utterances

A computational model for unsupervised word discovery,” in Order: A

Abstract We present an unsupervised algorithm for the discovery of words and word-like fragments from the speech signal, without using an upfront defined lexicon or acoustic phone models. The algorithm is based on a combination of acoustic pattern discovery, clustering, and temporal sequence learning. It exploits the acoustic similarity between multiple acoustic tokens of the same words or word-like fragments. In its current form, the algorithm is able to discover words in speech with low perplexity (connected digits). Although its performance still falls off compared to mainstream ASR approaches, the value of the algorithm is its potential to serve as a computational model in two research directions. First, the algorithm may lead to an approach for speech recognition that is fundamentally liberated from the modelling constraints in conventional ASR. Second, the proposed algorithm can be interpreted as a computational model of language acquisition that takes actual speech as input and is able to find words as 'emergent' properties from raw input