scholarly journals Incorporating Uncertainty into Deep Learning for Spoken Language Assessment

2017 ◽  
Author(s):  
Andrey Malinin ◽  
Anton Ragni ◽  
Kate Knill ◽  
Mark Gales
Keyword(s):  
Deep Learning ◽  
Language Assessment ◽  
Spoken Language

scholarly journals Ensemble Approaches for Uncertainty in Spoken Language Assessment

2020 ◽  
Author(s):  
Xixin Wu ◽  
Kate M. Knill ◽  
Mark J.F. Gales ◽  
Andrey Malinin
Keyword(s):  
Language Assessment ◽  
Spoken Language

The Handbook of Language Assessment Across Modalities

2021 ◽  
Keyword(s):  
Second Language ◽  
Language Assessment ◽  
Spoken Language ◽  
Assessment Literacy ◽  
Signed Language ◽  
Key Issues ◽  
Relevant Field ◽  
The Common ◽  
New Perspective ◽  
First Time

In Language Assessment Across Modalities: Paired-Papers on Signed and Spoken Language Assessment, volume editors Tobias Haug, Wolfgang Mann, and Ute Knoch bring together—for the first time—researchers, clinicians, and practitioners from two different fields: signed language and spoken language. The volume examines theoretical and practical issues related to 12 topics ranging from test development and language assessment of bi-/multilingual learners to construct issues of second-language assessment (including the Common European Framework of Reference [CEFR]) and language assessment literacy in second-language assessment contexts. Each topic is addressed separately for spoken and signed language by experts from the relevant field. This is followed by a joint discussion in which the chapter authors highlight key issues in each field and their possible implications for the other field. What makes this volume unique is that it is the first of its kind to bring experts from signed and spoken language assessment to the same table. The dialogues that result from this collaboration not only help to establish a shared appreciation and understanding of challenges experienced in the new field of signed language assessment but also breathes new life into and provides a new perspective on some of the issues that have occupied the field of spoken language assessment for decades. It is hoped that this will open the door to new and exciting cross-disciplinary collaborations.

Discussion of Issues Related to Assessment of Signed or Spoken Language Development in Children with Autism Spectrum Disorder

2021 ◽  
pp. 145-152
Author(s):  
Amy Kissel Frisbie ◽  
Aaron Shield ◽  
Deborah Mood ◽  
Nicole Salamy ◽  
Jonathan Henner
Keyword(s):  
Language Development ◽  
Developmental Disorders ◽  
Language Assessment ◽  
Spoken Language ◽  
Autism Spectrum ◽  
Pragmatic Language ◽  
Signed Language ◽  
Pragmatic Language Skills ◽  
Language Assessments ◽  
Hearing Children

This chapter is a joint discussion of key items presented in Chapters 4.1 and 4.2 related to the assessment of deaf and hearing children on the autism spectrum . From these chapters it becomes apparent that a number of aspects associated with signed language assessment are relevant to spoken language assessment. For example, there are several precautions to bear in mind about language assessments obtained via an interpreter. Some of these precautions apply solely to D/HH children, while others are applicable to assessments with hearing children in multilingual contexts. Equally, there are some aspects of spoken language assessment that can be applied to signed language assessment. These include the importance of assessing pragmatic language skills, assessing multiple areas of language development, differentiating between ASD and other developmental disorders, and completing the language evaluation within a developmental framework. The authors conclude with suggestions for both spoken and signed language assessment.

Discussion on Scoring Issues in Second Signed or Spoken Language Assessment

2021 ◽  
pp. 329-332
Author(s):  
Tobias Haug ◽  
Ute Knoch ◽  
Wolfgang Mann
Keyword(s):  
Second Language ◽  
Nonverbal Communication ◽  
Rasch Analysis ◽  
Language Assessment ◽  
Spoken Language ◽  
Rater Training ◽  
Signed Language ◽  
Language Assessments ◽  
New Research

This chapter is a joint discussion of key items related to scoring issues related to signed and spoken language assessment that were discussed in Chapters 9.1 and 9.2. One aspect of signed language assessment that has the potential to stimulate new research in spoken second language (L2) assessment is the scoring of nonverbal speaker behaviors. This aspect is rarely represented in the scoring criteria of spoken assessments and in many cases not even available to raters during the scoring process. The authors argue, therefore, for a broadening of the construct of spoken language assessment to also include elements of nonverbal communication in the scoring descriptors. Additionally, the importance of rater training for signed language assessments, application of Rasch analysis to investigate possible reasons of disagreement between raters, and the need to conduct research on rasting scales are discussed.

scholarly journals Indian Language Identification using Deep Learning

2020 ◽  
Vol 32 ◽  
pp. 01010
Author(s):  
Shubham Godbole ◽  
Vaishnavi Jadhav ◽  
Gajanan Birajdar
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Visual Representations ◽  
Spoken Language ◽  
Language Identification ◽  
Indian Language ◽  
Regular Method ◽  
Speech Database ◽  
Day By Day

Spoken language is the most regular method of correspondence in this day and age. Endeavours to create language recognizable proof frameworks for Indian dialects have been very restricted because of the issue of speaker accessibility and language readability. However, the necessity of SLID is expanding for common and safeguard applications day by day. Feature extraction is a basic and important procedure performed in LID. A sound example is changed over into a spectrogram visual portrayal which describes a range of frequencies in regard with time. Three such spectrogram visuals were generated namely Log Spectrogram, Gammatonegram and IIR-CQT Spectrogram for audio samples from the standardized IIIT-H Indic Speech Database. These visual representations depict language specific details and the nature of each language. These spectrograms images were then used as an input to the CNN. Classification accuracy of 98.86% was obtained using the proposed methodology.

Deep learning for spoken language identification: Can we visualize speech signal patterns?

2019 ◽  
Vol 31 (12) ◽  
pp. 8483-8501 ◽  
Author(s):  
Himadri Mukherjee ◽  
Subhankar Ghosh ◽  
Shibaprasad Sen ◽  
Obaidullah Sk Md ◽  
K. C. Santosh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speech Signal ◽  
Spoken Language ◽  
Language Identification
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