Children story classification based on structure of the story

Harikrishna, D M; Rao, Kanishka

doi:10.1109/icacci.2015.7275822

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…For Hindi children stories text classifications are shown in [22] and [23]. Similar approached is followed for manually annotating the story-corpus based on the three discourse modes.…”

Section: A Analysis Of Discourse Modesmentioning

confidence: 99%

Data-driven pause prediction for synthesis of storytelling style speech based on discourse modes

Sarkar

Rao

2015

2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

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In storytelling style, a storyteller generally uses prosodic variations with subtle speech nuances for the better apprehension of the listeners. It is achieved by emphasizing prominent words, using various emotions, mimicking voices and providing appropriate pauses. This work is a part of building the Story Text-to-Speech (TTS) [1] synthesis systems in Indian Languages, which aims at synthesizing the storytelling style speech from the neutral TTS. The neutral speech is converted to storytelling style by modifying the specific prosodic parameters (i.e. duration, pitch, tempo, intensity and pauses). The main contribution of this paper is to model the pause patterns present in storytelling style speech based on the modes of discourse: narrative, descriptive and dialogue to capture the story-semantic information. Analysis of pause patterns are carried out for children stories in Hindi language. We analyzed the pause patterns and classified pauses into three different categories: short, medium and long pauses for each mode of discourse. A three stage data-driven method is proposed to predict the position and duration of the pauses. We conducted objective test to evaluate the performance of the proposed method at each stage. Also, subjective evaluation is carried out on the final output of the Hindi Story-TTS system. The subjective evaluation connotes that the subjects have perceived an improvement in speech quality in terms of storytelling style.

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“…For Hindi children stories text classifications are shown in [22] and [23]. Similar approached is followed for manually annotating the story-corpus based on the three discourse modes.…”

Section: A Analysis Of Discourse Modesmentioning

confidence: 99%

Data-driven pause prediction for synthesis of storytelling style speech based on discourse modes

Sarkar

Rao

2015

2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

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“…Dubey (2014) proposed a Hindi-Dogri MT system and achieved 95% accuracy using bilingual dictionary. Harikrishna and Rao (2015) extracted Term Frequency (TF), TF inverse document frequency and Density of Part-Of-Speech (PD) features to classify Hindi and Telugu children stories. For this, they used Naive Bayes, KNN and SVM classifiers and achieved 0.88 and 0.85 F-measures with TF + PD features using Naive Bayes and SVM both for Hindi and Telugu languages, respectively.…”

Section: Study Ii: a Review Of Bilingual Processing Systems With Othe...mentioning

confidence: 99%

Advanced Applications on Bilingual Document Analysis and Processing Systems

Puri

Singh

2020

International Journal of Applied Metaheuristic Computing

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Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

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“…De acuerdo con Grimmer y Stewart [6], quienes han estudiado el análisis de textos políticos, es apropiado emplear técnicas de clasificación de textos supervisadas si se conocen con antelación las categorías dentro de las cuales serán clasificados los textos. En algunos trabajos relacionados vemos cómo se clasifican cuentos infantiles en las categorías de fábula, cuento popular y leyenda [7] o cómo se identifican las categorías de las historias dentro un conjunto cuentos populares [8]. En el caso de historias provenientes de familiares de víctimas y sobrevivientes del conflicto sociopolítico de Colombia, en primer lugar, se deberían identificar dichas categorías y luego encontrar un método apropiado para realizar la clasificación.…”

Section: Oportunidad Y Problemáticaunclassified

PA163-2-CLASHISTVI : clasificación de historias provenientes de familiares de víctimas y sobrevivientes del conflicto sociopolítico de Colombia

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El presente trabajo ilustra cómo se desarrolló un prototipo de software para clasificación su-pervisada de información no estructurada asociada a textos de historias provenientes de familiares de víctimas y sobrevivientes del conflicto sociopolítico de Colombia. Técnicas de proce-samiento natural del leguaje, como name entity recognition , permiten definir en cifras por-centuales las categorías a las que pertenece un texto. Son aplicadas en conjunto las metodo-logías Design Science Research y CRISP-DM y se realiza una evaluación enfocada en el usuario siguiendo Technology Acceptance Model (TAM) .

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Children story classification based on structure of the story

Cited by 11 publications

References 10 publications

Data-driven pause prediction for synthesis of storytelling style speech based on discourse modes

Data-driven pause prediction for synthesis of storytelling style speech based on discourse modes

Advanced Applications on Bilingual Document Analysis and Processing Systems

PA163-2-CLASHISTVI : clasificación de historias provenientes de familiares de víctimas y sobrevivientes del conflicto sociopolítico de Colombia

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