A Review of Shorthand Systems: From Brachygraphy to Microtext and Beyond

Satapathy, Ranjan; Wang, Zhaoxia; Нанетти, Андреа; Hussain, Amir

doi:10.1007/s12559-020-09723-7

Cited by 28 publications

(12 citation statements)

References 42 publications

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“…e Canberra distance between adjacent frames of lip texture features is utilized as visual dynamic features. Experimental evaluation of the Chinese corpus shows that visual dynamic texture features outperform static features in visual speech recognition [7]. Compared with the commonly used Canberra distance features, the dynamic texture features can improve the word correct rate by about 8%.…”

Section: Related Jobsmentioning

confidence: 97%

Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing

Zheng

Hao

et al. 2022

Computational Intelligence and Neuroscience

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This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm.

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Section: Related Jobsmentioning

confidence: 97%

Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing

Zheng

Hao

et al. 2022

Computational Intelligence and Neuroscience

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“…In order to accurately extract and manipulate text meaning, a Natural language processing (NLP) system must have access to a notable amount of knowledge about the world and the domain of discourse. With the data, the NLP system is reliant on the extraction of meaning from text that has resulted in an exponential interest in NLP tasks such as sentiment analysis [1], microtext normalization [2], and others. The rise of ecommerce has given rise to reliance on sentiment analysis for market research.…”

Section: Introductionmentioning

confidence: 99%

Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

Satapathy¹,

Pardeshi²,

Wang

2022

Future Internet

Self Cite

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In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propose a knowledge-sharing-based multitask learning framework. To ensure high-quality knowledge sharing between the tasks, we use the Neural Tensor Network, which consists of a bilinear tensor layer that links the two entity vectors. We show that BERT-based embedding with our MTL framework outperforms the baselines and achieves a new state-of-the-art status in multitask learning. Our framework shows that the information across datasets for related tasks can be helpful for understanding task-specific features.

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“…In order to accurately extract and manipulate text meaning, an NLP system must have access to a notable amount of knowledge about the world and the domain of discourse. With the data, the NLP system is reliant on the extraction of meaning from text which has resulted in an exponential interest in NLP tasks like sentiment analysis [1,2], microtext normalization [3] and others. The tasks in NLP are interrelated and could benefit from sharing each other's learning.…”

Section: Introductionmentioning

confidence: 99%

Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

Satapathy¹,

Pardeshi²,

Wang³

2022

Preprint

Self Cite

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Multitask learning often helps improve the performance of related tasks as these often have interdependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multitask learning framework that jointly performs polarity and subjective detection. We propose an attention-based multitask model for predicting polarity and subjectivity. The input sentences are transformed into vectors using pre-trained BERT and Glove embeddings, and the results depict that BERT embedding based model works better than the Glove based model. We compare our approach with state-of-the-art models in both subjective and polarity classification single-task and multitask frameworks. The proposed approach reports baseline performances for both polarity detection and subjectivity detection.

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A Review of Shorthand Systems: From Brachygraphy to Microtext and Beyond

Cited by 28 publications

References 42 publications

Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing

Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing

Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

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