Automatic Sarcasm Detection with Textual and Acoustic Data

This paper takes focus on the area of automatic sarcasm detection. Automatic sarcasm detection is crucial due to the needs of sentimental analysis. The rapid development of automatic speech recognition and text mining and the large amount of voice and text data opens a broader way for researchers to open new method and improves the accuracy of automatic sarcasm detection. We observe approaches that have been used to detect sarcasm, kind of data and its features including the rises of context to improve the accuracy of automatic sarcasm detection. We found that some context cannot be reliable without the presence of other context and some approaches are very dependent on the dataset. Twitter is being used by researchers as the main mine for sentimental analysis, we notice that at some aspect it still has a flaw because it is dependent to some Twitter’s special feature that will not be found in other usual text data like hashtags and author history. Besides that, we see that the small amount of research about automatic sarcasm detection through acoustic data and its correlation with textual data could make a new opportunity in the area of sarcasm detection in speech. From acoustic data, we could get both acoustic features and textual features. Sarcasm detection with voice has the potential to get higher accuracy since it can be extracted into two data types. By describing each beneficial method, this paper could be a brief way to sarcasm detection through acoustic and textual data.

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Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

Journal Of Big Data ◽

10.1186/s40537-020-00391-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Asmaa El Hannani ◽

Rahhal Errattahi ◽

Fatima Zahra Salmam ◽

Thomas Hain ◽

Hassan Ouahmane

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Error Detection ◽

State Of The Art ◽

Rapid Development ◽

Unified Framework ◽

Human Machine Interaction ◽

Detection Analysis ◽

Extensive Evaluation ◽

Effectiveness And Efficiency

AbstractSpeech based human-machine interaction and natural language understanding applications have seen a rapid development and wide adoption over the last few decades. This has led to a proliferation of studies that investigate Error detection and classification in Automatic Speech Recognition (ASR) systems. However, different data sets and evaluation protocols are used, making direct comparisons of the proposed approaches (e.g. features and models) difficult. In this paper we perform an extensive evaluation of the effectiveness and efficiency of state-of-the-art approaches in a unified framework for both errors detection and errors type classification. We make three primary contributions throughout this paper: (1) we have compared our Variant Recurrent Neural Network (V-RNN) model with three other state-of-the-art neural based models, and have shown that the V-RNN model is the most effective classifier for ASR error detection in term of accuracy and speed, (2) we have compared four features’ settings, corresponding to different categories of predictor features and have shown that the generic features are particularly suitable for real-time ASR error detection applications, and (3) we have looked at the post generalization ability of our error detection framework and performed a detailed post detection analysis in order to perceive the recognition errors that are difficult to detect.

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Improving Aphasic Speech Recognition by Using Novel Semi-Supervised Learning Methods on AphasiaBank for English and Spanish

Applied Sciences ◽

10.3390/app11198872 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8872

Author(s):

Iván G. Torre ◽

Mónica Romero ◽

Aitor Álvarez

Keyword(s):

Speech Recognition ◽

Supervised Learning ◽

Automatic Speech Recognition ◽

English Language ◽

Spanish Language ◽

Learning Methods ◽

Text Data ◽

Lower Performance ◽

Recognition Systems ◽

Fine Tune

Automatic speech recognition in patients with aphasia is a challenging task for which studies have been published in a few languages. Reasonably, the systems reported in the literature within this field show significantly lower performance than those focused on transcribing non-pathological clean speech. It is mainly due to the difficulty of recognizing a more unintelligible voice, as well as due to the scarcity of annotated aphasic data. This work is mainly focused on applying novel semi-supervised learning methods to the AphasiaBank dataset in order to deal with these two major issues, reporting improvements for the English language and providing the first benchmark for the Spanish language for which less than one hour of transcribed aphasic speech was used for training. In addition, the influence of reinforcing the training and decoding processes with out-of-domain acoustic and text data is described by using different strategies and configurations to fine-tune the hyperparameters and the final recognition systems. The interesting results obtained encourage extending this technological approach to other languages and scenarios where the scarcity of annotated data to train recognition models is a challenging reality.

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Incorporating Text OLAP in Business Intelligence

Business Intelligence Applications and the Web - Advances in Business Information Systems and Analytics ◽

10.4018/978-1-61350-038-5.ch004 ◽

2011 ◽

pp. 77-101 ◽

Cited By ~ 1

Author(s):

Byung-Kwon Park ◽

Il-Yeol Song

Keyword(s):

Information Retrieval ◽

Text Mining ◽

Business Intelligence ◽

Multidimensional Analysis ◽

Web Pages ◽

Data Types ◽

Text Documents ◽

Text Data ◽

Platform Architecture ◽

Unstructured Text

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.

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The Method to Analyze Freely Described Data from Questionnaires

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2009.p0268 ◽

2009 ◽

Vol 13 (3) ◽

pp. 268-274 ◽

Cited By ~ 3

Author(s):

Masaomi Kimura ◽

Keyword(s):

Text Mining ◽

Call Center ◽

Clustering Algorithms ◽

Web Pages ◽

Research Papers ◽

Text Data ◽

Textual Data ◽

The Way ◽

Newspaper Articles

Text mining has been growing; mainly due to the need to extract useful information from vast amounts of textual data. Our target here is text data, a collection of freely described data from questionnaires. Unlike research papers, newspaper articles, call-center logs and web pages, which are usually the targets of text mining analysis, the freely described data contained in the questionnaire responses have specific characteristics, including a small number of short sentences forming individual pieces of data, while the wide variety of content precludes the applications of clustering algorithms used to classify the same. In this paper, we suggest the way to extract the opinions which are delivered by multiple respondents, based on the modification relationships included in each sentence in the freely described data. Certain applications of our method are also presented after the introduction of our approach.

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