Statistical Parametric Methods for Articulatory-Based Foreign Accent Conversion

Foreign accent conversion seeks to transform utterances from a non-native speaker (L2) to appear as if they had been produced by the same speaker but with a native (L1) accent. Such accent-modified utterances have been suggested to be effective in pronunciation training for adult second language learners. Accent modification involves separating the linguistic gestures and voice-quality cues from the L1 and L2 utterances, then transposing them across the two speakers. However, because of the complex interaction between these two sources of information, their separation in the acoustic domain is not straightforward. As a result, vocoding approaches to accent conversion results in a voice that is different from both the L1 and L2 speakers. In contrast, separation in the articulatory domain is straightforward since linguistic gestures are readily available via articulatory data. However, because of the difficulty in collecting articulatory data, conventional synthesis techniques based on unit selection are ill-suited for accent conversion given the small size of articulatory corpora and the inability to interpolate missing native sounds in L2 corpus. To address these issues, this dissertation presents two statistical parametric methods to accent conversion that operate in the acoustic and articulatory domains, respectively. The acoustic method uses a cross-speaker statistical mapping to generate L2 acoustic features from the trajectories of L1 acoustic features in a reference utterance. Our results show significant reductions in the perceived non-native accents compared to the corresponding L2 utterance. The results also show a strong voice-similarity between accent conversions and the original L2 utterance. Our second (articulatory-based) approach consists of building a statistical parametric articulatory synthesizer for a non-native speaker, then driving the synthesizer with the articulators from the reference L1 speaker. This statistical approach not only has low data requirements but also has the flexibility to interpolate missing sounds in the L2 corpus. In a series of listening tests, articulatory accent conversions were rated more intelligible and less accented than their L2 counterparts. In the final study, we compare the two approaches: acoustic and articulatory. Our results show that the articulatory approach, despite the direct access to the native linguistic gestures, is less effective in reducing perceived non-native accents than the acoustic approach

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3–4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind

Cost-benefit analysis and resource use efficiency of rice production system in different agriculture landscapes in Chitwan district, Nepal

The study was conducted to determine the cost-benefit analysis and resource use efficiency of the rice production system in different agriculture landscapes in the Chitwan district in 2018. The sample size of 102 rice-growing farmers out of 600 farmers, having an area of farm size greater than 0.5 hectares, was determined using Raosoft Inc. Software. A simple random sampling technique was used to collect 102 rice-growing household information in four municipalities (2 in plain and 2 in hilly area) using a semi-structured questionnaire. Data were analyzed using descriptive and statistical tools including Cobb-Douglas production function. Results showed that the use of inputs like seeds, chemical fertilizer and machinery like tractor were found significantly higher in the plain area whereas the use of inputs like labor, farmyard manure (FYM) and bullocks was found in higher in the hilly area. The costs of fertilizer, machinery, pesticide, and transportation were found higher in the plain area whereas the costs of seed, FYM, labor and bullocks were significantly higher in the hilly area. Production of rice per household was 1.87 ton whereas productivity was 5.2 ton/ha, gross profit was NRs. 41435and benefit-cost ratio was 1.59 in the plain area which was found significantly higher than the hilly area. The return to scale was found to be 0.48 which revealed that inputs used in rice production were ineffectively utilized in which organic fertilizer and labor resource were overused and seed, fertilizer, machinery and bullocks, pesticides and transportation were underused resources. The optimal allocation of these resources will increase the profitability of rice farming

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3-4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.</p

Association of vaccination status with the clinicobiochemical profile, hospital stay, and mortality in COVID‐19: A case–control study

Abstract Background and Aims The effectiveness of coronavirus disease 2019 (COVID‐19) vaccines in reducing symptoms, disease advancement, complications, and mortality in severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection has been well‐established. This case‐control study aimed to compare different blood parameters, and prognostic and survival outcomes of COVID‐19 patients based on vaccination status. Methods We performed a case‐control study that included hospitalized patients with COVID‐19 at Tribhuvan University Teaching Hospital, Kathmandu, Nepal. Individuals who received vaccination were designated as cases and unvaccinated individuals as controls. Demographics, co‐morbidity, clinical data, laboratory data, and disease outcomes were recorded for both groups. Multivariate, Cox, and linear regression were used for analysing blood parameters, hospital admission, survival, and hospital stay, respectively, between cases and controls. Results Out of 100 participants enrolled, 46 were vaccinated, and 54 weren't. At admission, ferritin and erythrocyte sedimentation rate (ESR) were significantly lower in cases. At discharge, cases showed a higher monocyte than controls. Ferritin, ESR, and d‐imer showed excellent performance in determining the severity of symptoms. Significant correlation and regression of ferritin and ESR with the length of hospital stay was observed. Length of hospital stay was significantly lower in cases than in controls. No significant differences between cases and controls were observed in mortality. Conclusion COVID‐19 vaccines effectively reduced hospitalization duration. Ferritin and ESR were significantly lower in vaccinated individuals and showed the best utility in monitoring the disease