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Examining the morphological decomposition of complex words in native and non-native speakers of English
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INGLIZ VA O'ZBEK TILLARIDA SINONIM SO'ZLARINING QIYOSIY TAXLILI ASNOSIDA LUG'AVIY ZAXIRANI RIVOJLANTIRISH ...
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ФАКТОР ДОВЕРИЯ В ДИСКУРСИВНОЙ ПРАКТИКЕ СОЦИАЛЬНОГО РАБОТНИКА ВЕЛИКОБРИТАНИИ ... : TRUST FACTOR IN THE DISCOURSE PRACTICE OF A SOCIAL WORKER IN GREAT BRITAIN ...
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Intoxication and pitch control in tonal and non-tonal language speakers ...
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К ВОПРОСУ О СОПОСТАВИТЕЛЬНОЙ ФРАЗЕОЛОГИИ ... : ON THE QUESTION OF COMPARATIVE PHRASEOLOGY ...
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Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features ...
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Crosslinguistic Influence in the Discrimination of Korean Stop Contrast by Heritage Speakers and Second Language Learners
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In: Languages; Volume 7; Issue 1; Pages: 6 (2022)
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The Emotion Probe: On the Universality of Cross-Linguistic and Cross-Gender Speech Emotion Recognition via Machine Learning
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In: Sensors; Volume 22; Issue 7; Pages: 2461 (2022)
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Abstract:
Machine Learning (ML) algorithms within a human–computer framework are the leading force in speech emotion recognition (SER). However, few studies explore cross-corpora aspects of SER; this work aims to explore the feasibility and characteristics of a cross-linguistic, cross-gender SER. Three ML classifiers (SVM, Naïve Bayes and MLP) are applied to acoustic features, obtained through a procedure based on Kononenko’s discretization and correlation-based feature selection. The system encompasses five emotions (disgust, fear, happiness, anger and sadness), using the Emofilm database, comprised of short clips of English movies and the respective Italian and Spanish dubbed versions, for a total of 1115 annotated utterances. The results see MLP as the most effective classifier, with accuracies higher than 90% for single-language approaches, while the cross-language classifier still yields accuracies higher than 80%. The results show cross-gender tasks to be more difficult than those involving two languages, suggesting greater differences between emotions expressed by male versus female subjects than between different languages. Four feature domains, namely, RASTA, F0, MFCC and spectral energy, are algorithmically assessed as the most effective, refining existing literature and approaches based on standard sets. To our knowledge, this is one of the first studies encompassing cross-gender and cross-linguistic assessments on SER.
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Keyword:
artificial intelligence; cross-gender; cross-linguistic; emotion recognition; English; machine learning; SER; speech; SVM
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URL: https://doi.org/10.3390/s22072461
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Preposition Stranding in Spanish–English Code-Switching
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In: Languages; Volume 7; Issue 1; Pages: 45 (2022)
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Corpus of Early English Correspondence Extension Sampler part 2 (CEECES 2) ...
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Tagged Corpus of Early English Correspondence Extension Sampler (TCEECES) ...
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Corpus of Early English Correspondence Extension Sampler part 1 (CEECES 1) ...
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Tagged Corpus of Early English Correspondence Extension Sampler (TCEECES) ...
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Corpus of Early English Correspondence Extension Sampler part 2 (CEECES 2) ...
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Corpus of Early English Correspondence Extension Sampler part 1 (CEECES 1) ...
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A tale of two committees: Newbolt illuminated through the Cox models ; The New Newbolt Report: One Hundred Years of Teaching English in England
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On the move: music and english together lead to effective CLIL ; En movimiento: música, inglés, AICLE efectivo
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Formación específica en AICLE en Educación Física para mejorar el tiempo de compromiso motor ; Specific Training in Clil in Physical Education to Improve Motor Engagement Time
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