Unsupervised extraction of recurring words from infant-directed speech

To date, most computational models of infant word segmentation have worked from phonemic or phonetic input, or have used toy datasets. In this paper, we present an algorithm for word extraction that works directly from naturalistic acoustic input: infant-directed speech from the CHILDES corpus. The algorithm identifies recurring acoustic patterns that are candidates for identification as words or phrases, and then clusters together the most similar patterns. The recurring patterns are found in a single pass through the corpus using an incremental method, where only a small number of utterances are considered at once. Despite this limitation, we show that the algorithm is able to extract a number of recurring words, including some that infants learn earliest, such as Mommy and the child’s name. We also introduce a novel information-theoretic evaluation measure

Feed Forward Pre-training for Recurrent Neural Network Language Models

The recurrent neural network language model (RNNLM) has been demonstrated to consistently reduce perplexities and au-tomatic speech recognition (ASR) word error rates across a variety of domains. In this paper we propose a pre-training method for the RNNLM, by sharing the output weights of the feed forward neural network language model (NNLM) with the RNNLM. This is accomplished by first fine-tuning the weights of the NNLM, which are then used to initialise the output weights of an RNNLM with the same number of hidden units. We have carried out text-based experiments on the Penn Tree-bank Wall Street Journal data, and ASR experiments on the TED talks data used in the International Workshop on Spoken Language Translation (IWSLT) evaluation campaigns. Across the experiments, we observe small improvements in perplexity and ASR word error rate

The UEDIN ASR Systems for the IWSLT 2014 Evaluation

This paper describes the University of Edinburgh (UEDIN) ASR systems for the 2014 IWSLT Evaluation. Notable fea-tures of the English system include deep neural network acoustic models in both tandem and hybrid configuration with the use of multi-level adaptive networks, LHUC adapta-tion and Maxout units. The German system includes lightly supervised training and a new method for dictionary gener-ation. Our voice activity detection system now uses a semi-Markov model to incorporate a prior on utterance lengths. There are improvements of up to 30 % relative WER on the tst2013 English test set. 1

Usability assessment of text-to-speech synthesis for additional detail in an automated telephone banking system

International audienceThis paper describes a comprehensive usability evaluation of an automated telephone banking system which employs text-to-speech (TTS) synthesis in offering additional detail on customers' account transactions. The paper describes a series of four experiments in which TTS was employed to offer an extra level of detail to recent transactions listings within an established banking service which otherwise uses recorded speech from a professional recording artist. Results from the experiments show that participants welcome the added value of TTS in being able to provide additional detail on their account transactions, but that TTS should be used minimally in the service

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification