Leisure activity associated with cognitive ability level, but not cognitive change

Although activity participation is promoted as cognitively-protective, critical questions of causality remain. In a cohort followed every five years from age 75 to 85 years, potential reciprocal associations between level and change in leisure activity participation and level and change in cognitive abilities were examined. Participants in the Glostrup 1914 Cohort, a longitudinal study of ageing, completed standardised cognitive ability tests and reported their leisure activity participation (11 activities defined a leisure activity score) at ages 75, 80 and 85. Higher leisure activity was associated with higher cognitive ability (significant correlations ranged from .15 to .31, p < .05). Between ages 75 and 85, participation in leisure activities and cognitive ability declined significantly. Growth curve models, which provided latent variables for level of and 10-year change in both leisure activity and cognitive ability, confirmed the positive association between levels of leisure activity and cognitive ability (path coefficient = .36, p < .001); however, leisure activity level or change was not associated with cognitive change. Although a positive association between leisure activity and cognitive ability was reported—the likely precedents of this are discussed—there was no evidence that a higher level or maintenance of leisure activity was protective against cognitive decline across a 10-year follow-up

Revitalizing the Retail Trade Sector in Rural Communities: Experiences of 13 North Dakota Towns

Community/Rural/Urban Development,

Towards Zero-shot Learning for Automatic Phonemic Transcription

Automatic phonemic transcription tools are useful for low-resource language documentation. However, due to the lack of training sets, only a tiny fraction of languages have phonemic transcription tools. Fortunately, multilingual acoustic modeling provides a solution given limited audio training data. A more challenging problem is to build phonemic transcribers for languages with zero training data. The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes. In this work, we address this problem by adopting the idea of zero-shot learning. Our model is able to recognize unseen phonemes in the target language without any training data. In our model, we decompose phonemes into corresponding articulatory attributes such as vowel and consonant. Instead of predicting phonemes directly, we first predict distributions over articulatory attributes, and then compute phoneme distributions with a customized acoustic model. We evaluate our model by training it using 13 languages and testing it using 7 unseen languages. We find that it achieves 7.7% better phoneme error rate on average over a standard multilingual model.Comment: AAAI 202

Universal Phone Recognition with a Multilingual Allophone System

Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages. Multilingual acoustic models, however, generally ignore the difference between phonemes (sounds that can support lexical contrasts in a particular language) and their corresponding phones (the sounds that are actually spoken, which are language independent). This can lead to performance degradation when combining a variety of training languages, as identically annotated phonemes can actually correspond to several different underlying phonetic realizations. In this work, we propose a joint model of both language-independent phone and language-dependent phoneme distributions. In multilingual ASR experiments over 11 languages, we find that this model improves testing performance by 2% phoneme error rate absolute in low-resource conditions. Additionally, because we are explicitly modeling language-independent phones, we can build a (nearly-)universal phone recognizer that, when combined with the PHOIBLE large, manually curated database of phone inventories, can be customized into 2,000 language dependent recognizers. Experiments on two low-resourced indigenous languages, Inuktitut and Tusom, show that our recognizer achieves phone accuracy improvements of more than 17%, moving a step closer to speech recognition for all languages in the world.Comment: ICASSP 202