Strong Electron-Phonon Interaction and Colossal Magnetoresistance in EuTiO3_3

At low temperatures, EuTiO$_3$ system has very large resistivities and exhibits colossal magnetoresistance. Based on a first principle calculation and the dynamical mean-field theory for small polaron we have calculated the transport properties of EuTiO$_3$. It is found that due to electron-phonon interaction the conduction band may form a tiny subband which is close to the Fermi level. The tiny subband is responsible for the large resistivity. Besides, EuTiO$_3$ is a weak antiferromagnetic material and its magnetization would slightly shift the subband via exchange interaction between conduction electrons and magnetic atoms. Since the subband is close to the Fermi level, a slight shift of its position gives colossal magnetoresistance.Comment: 6 pages, 5 figure

Large adiabatic temperature and magnetic entropy changes in EuTiO3

We have investigated the magnetocaloric effect in single and polycrystalline samples of quantum paraelectric EuTiO3 by magnetization and heat capacity measurements. Single crystalline EuTiO3 shows antiferromagnetic ordering due to Eu2+ magnetic moments below TN = 5.6 K. This compound shows a giant magnetocaloric effect around its Neel temperature. The isothermal magnetic entropy change is 49 Jkg-1K-1, the adiabatic temperature change is 21 K and the refrigeration capacity is 500 JKg-1 for a field change of 7 T at TN. The single crystal and polycrystalline samples show similar values of the magnetic entropy change and adiabatic temperature changes. The large magnetocaloric effect is due to suppression of the spin entropy associated with localized 4f moment of Eu2+ ions. The giant magnetocaloric effect together with negligible hysteresis, suggest that EuTiO3 could be a potential material for magnetic refrigeration below 20 K.Comment: 12 pages, 4 figure

Non-native children's automatic speech recognition: The INTERSPEECH 2020 shared task ALTA systems

Automatic spoken language assessment (SLA) is a challenging problem due to the large variations in learner speech combined with limited resources. These issues are even more problematic when considering children learning a language, with higher levels of acoustic and lexical variability, and of code-switching compared to adult data. This paper describes the ALTA system for the INTERSPEECH 2020 Shared Task on Automatic Speech Recognition for Non-Native Children’s Speech. The data for this task consists of examination recordings of Italian school children aged 9-16, ranging in ability from minimal, to basic, to limited but effective command of spoken English. A variety of systems were developed using the limited training data available, 49 hours. State-of-the-art acoustic models and language models were evaluated, including a diversity of lexical representations, handling code-switching and learner pronunciation errors, and grade specific models. The best single system achieved a word error rate (WER) of 16.9% on the evaluation data. By combining multiple diverse systems, including both grade independent and grade specific models, the error rate was reduced to 15.7%. This combined system was the best performing submission for both the closed and open tasks

Impact of ASR performance on spoken grammatical error detection

Computer assisted language learning (CALL) systems aidlearners to monitor their progress by providing scoring andfeedback on language assessment tasks. Free speaking tests al-low assessment of what a learner has said, as well as how theysaid it. For these tasks, Automatic Speech Recognition (ASR)is required to generate transcriptions of a candidate’s responses,the quality of these transcriptions is crucial to provide reliablefeedback in downstream processes. This paper considers theimpact of ASR performance on Grammatical Error Detection(GED) for free speaking tasks, as an example of providing feed-back on a learner’s use of English. The performance of an ad-vanced deep-learning based GED system, initially trained onwritten corpora, is used to evaluate the influence of ASR errors.One consequence of these errors is that grammatical errors canresult from incorrect transcriptions as well as learner errors, thismay yield confusing feedback. To mitigate the effect of theseerrors, and reduce erroneous feedback, ASR confidence scoresare incorporated into the GED system. By additionally adaptingthe written text GED system to the speech domain, using ASRtranscriptions, significant gains in performance can be achieved.Analysis of the GED performance for different grammatical er-ror types and across grade is also presented.ALT

Use of graphemic lexicons for spoken language assessment

Copyright © 2017 ISCA. Automatic systems for practice and exams are essential to support the growing worldwide demand for learning English as an additional language. Assessment of spontaneous spoken English is, however, currently limited in scope due to the difficulty of achieving sufficient automatic speech recognition (ASR) accuracy. "Off-the-shelf" English ASR systems cannot model the exceptionally wide variety of accents, pronunications and recording conditions found in non-native learner data. Limited training data for different first languages (L1s), across all proficiency levels, often with (at most) crowd-sourced transcriptions, limits the performance of ASR systems trained on non-native English learner speech. This paper investigates whether the effect of one source of error in the system, lexical modelling, can be mitigated by using graphemic lexicons in place of phonetic lexicons based on native speaker pronunications. Graphemicbased English ASR is typically worse than phonetic-based due to the irregularity of English spelling-to-pronunciation but here lower word error rates are consistently observed with the graphemic ASR. The effect of using graphemes on automatic assessment is assessed on different grader feature sets: audio and fluency derived features, including some phonetic level features; and phone/grapheme distance features which capture a measure of pronunciation ability

Sequence Teacher-Student Training of Acoustic Models for Automatic Free Speaking Language Assessment

A high performance automatic speech recognition (ASR) system is an important constituent component of an automatic language assessment system for free speaking language tests. The ASR system is required to be capable of recognising non-native spontaneous English speech and to be deployable under real-time conditions. The performance of ASR systems can often be significantly improved by leveraging upon multiple systems that are complementary, such as an ensemble. Ensemble methods, however, can be computationally expensive, often requiring multiple decoding runs, which makes them impractical for deployment. In this paper, a lattice-free implementation of sequence-level teacher-student training is used to reduce this computational cost, thereby allowing for real-time applications. This method allows a single student model to emulate the performance of an ensemble of teachers, but without the need for multiple decoding runs. Adaptations of the student model to speakers from different first languages (L1s) and grades are also explored.Cambridge Assessment Englis

Joint decoding of tandem and hybrid systems for improved keyword spotting on low resource languages

Copyright © 2015 ISCA. Keyword spotting (KWS) for low-resource languages has drawn increasing attention in recent years. The state-of-the-art KWS systems are based on lattices or Confusion Networks (CN) generated by Automatic Speech Recognition (ASR) systems. It has been shown that considerable KWS gains can be obtained by combining the keyword detection results from different forms of ASR systems, e.g., Tandem and Hybrid systems. This paper investigates an alternative combination scheme for KWS using joint decoding. This scheme treats a Tandem system and a Hybrid system as two separate streams, and makes a linear combination of individual acoustic model log-likelihoods. Joint decoding is more efficient as it requires just a single pass of decoding and a single pass of keyword search. Experiments on six Babel OP2 development languages show that joint decoding is capable of providing consistent gains over each individual system. Moreover, it is possible to efficiently rescore the joint decoding lattices with Tandem or Hybrid acoustic models, and further KWS gains can be obtained by merging the detection posting lists from the joint decoding lattices and rescored lattices