A deep learning approaches in text-to-speech system: a systematic review and recent research perspective

Kumar, Yogesh; Koul, Apeksha; Singh, Chamkaur

doi:10.1007/s11042-022-13943-4

Cited by 44 publications

(16 citation statements)

References 91 publications

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“…Distinct from chatbots, Kumar, Koul, and Singh (2023) have noted that AI-assisted text-to-speech tools have piqued researchers' interest in exploring various facets of human-computer interaction. Text-to-speech AI platforms have, in parallel, facilitated substantial advancements in computer-mediated interactions.…”

Section: Ai Assisted Text-to-speech and Speech-to-text Toolsmentioning

confidence: 99%

Educators’ Academic Insights on Artificial Intelligence: Challenges and Opportunities

Jose,

Jayaron Jose

2024

EJEL

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The study on " Educators’ Academic Insights on Artificial Intelligence – Challenges and Opportunities" was conducted to gain a deeper understanding of the rapidly evolving phenomenon of AI in education. This research serves multiple objectives. Firstly, it aims to foster awareness regarding the integration of AI into teaching and learning practices by providing clear definitions of AI and explaining key AI-related terms. It also seeks to illustrate AI's diverse applications within a broader context, with a special focus on AI-supported research and learning platforms. Additionally, the study delves into the current discourse surrounding chatbots, contributing to address the central research question. Lastly, this initiative aims to provide valuable recommendations for effectively harnessing AI in education, enhancing the teaching and learning experience. The researchers conducted a review of literature concerning artificial intelligence. They adopted a qualitative method, using open-ended questions to collect feedback from educators globally, including those from the University of Technology and Applied Sciences, Al Musannah, and participants in the online discussion forum at Oxford English Learning Exchange.com. The qualitative data was analysed, leading to the identification of key themes and subthemes derived from the responses of research participants. The study's findings incorporated a wide range of concerns expressed by educators, comprising ten key subthemes. These concerns ranged from doubts about AI's ability to replace human educators and fears of its potential to hinder student development to worries about its hyped popularity and its perceived futuristic nature. Educators stressed the importance of effective AI training while emphasizing the need to prioritize human expertise over excessive reliance on AI. They were also acutely aware of both the advantages and disadvantages of AI, viewing it as both a potential boon and a looming threat. Furthermore, educators recognized the potential for enjoyable experiences with AI and acknowledged the pivotal role of users in determining the extent of AI adoption. Content analysis revealed additional apprehensions, such as concerns about job displacement, AI's impact on critical thinking, teacher frustration in assessing AI-assisted student writing, the use of AI-generated content for assessments, potential erosion of human services, stifling of user and learner creativity by AI, the risk of errors in AI-generated information, opportunities for cheating in exams, and concerns about the overreliance on and overrating of AI platforms. Positively, the findings included an array of opportunities that AI platforms offer. Study participants highlighted various aspects of these opportunities that surpassed their concerns and associated risks. The opportunities are categorized into twenty subthemes: enhancing learner motivation, facilitating template creation, utilizing AI as an educational aid, promoting proper training and fostering positive AI usage, harnessing AI for teaching challenging subjects, enabling personalized learning experiences, offering an interactive tutoring experience, supporting remote learning, facilitating self-study, providing comprehensive educational content overviews, giving instantaneous feedback and evaluation, functioning as search engines and chatbots, enabling content validation, efficiency in terms of cost and time, streamlining material preparation, facilitating skill and language enhancement, promoting familiarity with topics and vocabulary, enabling text-to-speech and speech-to-text conversions, editing multimedia elements, and facilitating content generation.

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Section: Ai Assisted Text-to-speech and Speech-to-text Toolsmentioning

confidence: 99%

Educators’ Academic Insights on Artificial Intelligence: Challenges and Opportunities

Jose,

Jayaron Jose

2024

EJEL

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“…p. ex. Hirose & Tao, 2015 ;Ning et al, 2019 ;Sini et al,2022 ;Ren et al, 2020 ;Nuthakki et al, 2023 ;Kumar, Koul & Singh, 2023 ;Tan, 2023).…”

Section: En Guise De Conclusionmentioning

confidence: 99%

Inférences au pays de la prosodie

Banyś

2023

NEO

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When it comes to language, it’s not just the grammatical structure and the literal meaning of words that matter. The way a predicate imposes inferences on its propositional arguments is crucial to understanding the true meaning of a message. However, these inferences are influenced by many factors, such as prosody, world knowledge, speakers’ expectations regarding language use, situational stereotypes, and other implicit or contextual elements.In this paper, we examine the inferential status of the verbs referred to by Karttunen as “implicative verbs”. On the one hand, two-way implicative verbs, and on the other, one-way implicative verbs. The latter have been little studied from this perspective. This will lead us to highlight the fundamental role, too often forgotten, of prosody and focus/theme in this type of analysis and in determining the inferential status of predicates. Our analyses show that, once prosody has been considered, the classification of theoretically possible verb inferences accepted until now needs to be modified. We do not have 4 groups of one-way implicative verbs, as has been argued, but 2, namely the groups: [+/+/– // –/–] [affirmed > true or false // denied > false] of the type être capable, pouvoir and [+/+/– // –/+] [affirmed > true or false // denied > true] of the type hésiter à.The remaining two groups, considered as distinct and autonomous one-way implicative verbs with the suggested characteristics: [+ + // – +/–] [affirmed > true // denied > true or false] of the type forcer to and [+ – // – +/–] [affirmed > false // denied > true or false] of the type refuser de, belong to the canonical groups of two-way implicative verbs, respectively: forcer à to the group of verbs of the type réussir à: [+/+ // –/–] [affirmed > true // denied > false] and refuser de to the group of verbs of the type oublier de: [+/– // –/+] affirmed > false // denied > true].Naturally, this classification differentiation, important as it is, only reflects the different behaviour of certain types of predicate, and this is the most important element of these analyses with a view to automating the recognition of predicate inferences.

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“…They identified 48 accessible corpora sources and classified them into five categories based on their primary purpose: multipurpose, speech, dialectal, image, and sentiment analysis. Kumar et al [33] examined the contributions of various researchers to Indian and non-Indian language TTS systems. In their review, [33] examined several language TTS systems based on quality criteria, including recognition rate, accuracy, TTS score, precision, recall, and F1-score.…”

Section: Introductionmentioning

confidence: 99%

“…Kumar et al [33] examined the contributions of various researchers to Indian and non-Indian language TTS systems. In their review, [33] examined several language TTS systems based on quality criteria, including recognition rate, accuracy, TTS score, precision, recall, and F1-score.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

Chemnad

Othman

2023

IEEE Access

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Although there are several speech synthesis models available for different languages tailored to specific domain requirements and applications, there is currently no readily available information on the latest trends in Arabic language speech synthesis. This can make it challenging for beginners to research and develop text-to-speech (TTS) systems for Arabic languages. To address this issue, this article provides a comprehensive overview of several scholars' contributions to the field of Arabic TTS, along with an examination of the unique features of the Arabic language and the corresponding challenges in creating TTS systems. Reporting only on papers discussing Arabic TTS, this systematic review evaluated the available literature published between 2000 and 2022. We conducted a systematic review in six databases using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to identify studies that addressed Arabic Text-to-Speech systems. Of a total of 3719 articles identified, only 36 (0.96%) articles met our search criteria. Bibliometric analyses of these studies were conducted and reported. The results highlight the main types of speech synthesis techniques used in TTS systems: concatenative, formant, deep neural network (DNN), hybrid models, and multiagent. The corpora used to develop these systems, as well as the diacritization techniques incorporated, evaluation techniques, and the results of the performance of the systems are reported. Subjective evaluation using the mean opinion score is most applied to measure the accuracy of systems. This study also identifies gaps in the literature and makes recommendations for future research directions.

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A deep learning approaches in text-to-speech system: a systematic review and recent research perspective

Cited by 44 publications

References 91 publications

Educators’ Academic Insights on Artificial Intelligence: Challenges and Opportunities

Educators’ Academic Insights on Artificial Intelligence: Challenges and Opportunities

Inférences au pays de la prosodie

Advancements in Arabic Text-to-Speech Systems: A 22-Year Literature Review

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