Word-level beam search decoding and correction algorithm (WLBS) for end-to-end ASR

2021 ◽  
Author(s):  
Zitha Sasindran ◽  
Prabhakar Venkata Tamma
Keyword(s):  
Beam Search ◽  
Correction Algorithm ◽  
Word Level ◽  
End To End

scholarly journals Conversation Modeling with Neural Network

2018 ◽  
pp. 1-10
Author(s):  
Jivan Y. Patil ◽  
Girish P. Potdar
Keyword(s):  
Neural Networks ◽  
Intelligent System ◽  
Large Data ◽  
Learning Problems ◽  
Beam Search ◽  
Training Set ◽  
Data Set ◽  
Domain Specific ◽  
Proposed Model ◽  
End To End

The ability to process, understand and interact in natural language carries high importance for building a Intelligent system, as it will greatly affect the way of communicating with the system. Deep Neural Networks (DNNs) have achieved excellent performance for many of machine learning problems and are widely accepted for applications in the field of computer vision and supervised  learning. Although DNNs work well with availability of large labeled training set, it cannot be used to map complex structures like sentences end-to-end. Existing approaches for conversational modeling are domain specific and require handcrafted rules. This paper proposes a simple approach based on use of neural networks’ recently proposed sequence to sequence framework. The proposed model generates reply by predicting sentence using chained probability for given sentence(s) in conversation. This model is trained end-to-end on large data set. Proposed approach uses Attention to focus text generation on intent of conversation as well as beam search to generate optimum output with some diversity.Primary findings show that model shows common sense reasoning on movie transcript data set.

scholarly journals An Algorithm for Natural Images Text Recognition Using Four Direction Features

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 971 ◽  
Author(s):  
Min Zhang ◽  
Yujin Yan ◽  
Hai Wang ◽  
Wei Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Images ◽  
Text Recognition ◽  
Textual Information ◽  
Word Level ◽  
End To End ◽  
Regular Text ◽  
Character Sequence ◽  
Feature Code

Irregular text has widespread applications in multiple areas. Different from regular text, irregular text is difficult to recognize because of its various shapes and distorted patterns. In this paper, we develop a multidirectional convolutional neural network (MCN) to extract four direction features to fully describe the textual information. Meanwhile, the character placement possibility is extracted as the weight of the four direction features. Based on these works, we propose the encoder to fuse the four direction features for the generation of feature code to predict the character sequence. The whole network is end-to-end trainable due to using images and word-level labels. The experiments on standard benchmarks, including the IIIT-5K, SVT, CUTE80, and ICDAR datasets, demonstrate the superiority of the proposed method on both regular and irregular datasets. The developed method shows an increase of 1.2% in the CUTE80 dataset and 1.5% in the SVT dataset, and has fewer parameters than most existing methods.

Contextual Speech Recognition in End-to-end Neural Network Systems Using Beam Search

2018 ◽  
Author(s):  
Ian Williams ◽  
Anjuli Kannan ◽  
Petar Aleksic ◽  
David Rybach ◽  
Tara Sainath
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Beam Search ◽  
Network Systems ◽  
End To End

Collecting Words: A Clinical Example of a Morphology-Focused Orthographic Intervention

2020 ◽  
Vol 51 (3) ◽  
pp. 544-560 ◽  
Author(s):  
Kimberly A. Murphy ◽  
Emily A. Diehm
Keyword(s):  
Written Language ◽  
Morphological Knowledge ◽  
English Orthography ◽  
Word Level ◽  
Novel Approach ◽  
Language Pathology ◽  
The One ◽  
Critical Intervention ◽  
Reading And Spelling ◽  
Reading And Spelling Difficulties

Purpose Morphological interventions promote gains in morphological knowledge and in other oral and written language skills (e.g., phonological awareness, vocabulary, reading, and spelling), yet we have a limited understanding of critical intervention features. In this clinical focus article, we describe a relatively novel approach to teaching morphology that considers its role as the key organizing principle of English orthography. We also present a clinical example of such an intervention delivered during a summer camp at a university speech and hearing clinic. Method Graduate speech-language pathology students provided a 6-week morphology-focused orthographic intervention to children in first through fourth grade ( n = 10) who demonstrated word-level reading and spelling difficulties. The intervention focused children's attention on morphological families, teaching how morphology is interrelated with phonology and etymology in English orthography. Results Comparing pre- and posttest scores, children demonstrated improvement in reading and/or spelling abilities, with the largest gains observed in spelling affixes within polymorphemic words. Children and their caregivers reacted positively to the intervention. Therefore, data from the camp offer preliminary support for teaching morphology within the context of written words, and the intervention appears to be a feasible approach for simultaneously increasing morphological knowledge, reading, and spelling. Conclusion Children with word-level reading and spelling difficulties may benefit from a morphology-focused orthographic intervention, such as the one described here. Research on the approach is warranted, and clinicians are encouraged to explore its possible effectiveness in their practice. Supplemental Material https://doi.org/10.23641/asha.12290687

Factors Influencing Cognate Performance for Young Multilingual Children's Vocabulary: A Research Synthesis

2020 ◽  
Vol 29 (4) ◽  
pp. 2170-2188
Author(s):  
Lindsey R. Squires ◽  
Sara J. Ohlfest ◽  
Kristen E. Santoro ◽  
Jennifer L. Roberts
Keyword(s):  
Test Validity ◽  
Reaction Times ◽  
Bilingual Children ◽  
Second Grade ◽  
High Quality Research ◽  
Word Level ◽  
Research Questions ◽  
Word Characteristics ◽  
Study Inclusion ◽  
Task Level

Purpose The purpose of this systematic review was to determine evidence of a cognate effect for young multilingual children (ages 3;0–8;11 [years;months], preschool to second grade) in terms of task-level and child-level factors that may influence cognate performance. Cognates are pairs of vocabulary words that share meaning with similar phonology and/or orthography in more than one language, such as rose – rosa (English–Spanish) or carrot – carotte (English–French). Despite the cognate advantage noted with older bilingual children and bilingual adults, there has been no systematic examination of the cognate research in young multilingual children. Method We conducted searches of multiple electronic databases and hand-searched article bibliographies for studies that examined young multilingual children's performance with cognates based on study inclusion criteria aligned to the research questions. Results The review yielded 16 articles. The majority of the studies (12/16, 75%) demonstrated a positive cognate effect for young multilingual children (measured in higher accuracy, faster reaction times, and doublet translation equivalents on cognates as compared to noncognates). However, not all bilingual children demonstrated a cognate effect. Both task-level factors (cognate definition, type of cognate task, word characteristics) and child-level factors (level of bilingualism, age) appear to influence young bilingual children's performance on cognates. Conclusions Contrary to early 1990s research, current researchers suggest that even young multilingual children may demonstrate sensitivity to cognate vocabulary words. Given the limits in study quality, more high-quality research is needed, particularly to address test validity in cognate assessments, to develop appropriate cognate definitions for children, and to refine word-level features. Only one study included a brief instruction prior to assessment, warranting cognate treatment studies as an area of future need. Supplemental Material https://doi.org/10.23641/asha.12753179

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