Emotional Tone Research Articles

Congruency and Emotional Valence Effects on Speech Production in Individuals with Parkinson's Disease.

Individuals with Parkinson's disease (PD) exhibit a variety of impairments in nonmotor symptoms including emotional processing and cognitive control that have implications for speech production. The present study sought to investigate whether impairments in cognitive processing in individuals with PD impact emotional sentence production as indicated by changes in speech rate. Thirty-six individuals (20 individuals with PD, 16 healthy controls) completed subtests 8A and 8B of the Florida Emotional Expressive Battery (FEEB) to elicit speech samples in five different emotional tones (happy, sad, angry, fear, and neutral). Sentences contained either semantically emotional or neutral information, resulting in conditions of congruency (same semantics-tone) and incongruency (different semantics-tone). Speech rate was impacted by the emotional tone of all participants. Individuals with PD demonstrated faster speech rates under conditions of conflicting semantic information than healthy older adults. Changes in speech rate under emotional conditions were not influenced by global measures of cognition or depression. The results of this study indicate that individuals with PD struggle to manage irrelevant information present during emotional speech production. Speech rate is a simple, easy-to-measure metric that may reflect cognitive processing impairments in PD.

Multilayer perceptron-based literature reading preferences predict anxiety and depression in university students.

This study aims to precisely model the nonlinear relationship between university students' literature reading preferences (LRP) and their levels of anxiety and depression using a multilayer perceptron (MLP) to identify reading-related risk factors affecting anxiety and depression among university students. In this cross-sectional study, an internet-based questionnaire was conducted among 2,092 undergraduate students (aged 18-22, 62.7% female, from seven provinces in China). Participants completed a customized questionnaire on their LRP, followed by standardized assessments of anxiety and depression using the Generalized Anxiety Disorder 7-item Scale and the Beck Depression Inventory, respectively. An MLP with residual connections was employed to establish the nonlinear relationship between LRP and anxiety and depression. The MLP model achieved an average accuracy of 86.8% for predicting non-anxious individuals and 81.4% for anxious individuals. In the case of depression, the model's accuracy was 90.1% for non-depressed individuals and 84.1% for those with depression. SHAP value analysis identified "Tense/Suspenseful-Emotional Tone," "War and Peace-Thematic Content," and "Infrequent Reading-Reading Habits" as the top contributors to anxiety prediction accuracy. Similarly, "Sad-Emotional Tone Preference," "Emotional Depictions-Thematic Content," and "Thought-Provoking-Emotional Tone" were the primary contributors to depression prediction accuracy. The MLP accurately models the nonlinear relationship between LRP and mental health in university students, indicating the significance of specific reading preferences as risk factors. The study underscores the importance of literature emotional tone and themes in mental health. LRP should be integrated into psychological assessments to help prevent anxiety and depression among university students.

BLACKPINK Endorsement Effect: Luxury Perfume Brand Image and Consumption Behavior of Chinese College Students

This study explores the impact of K-pop stars on the brand image of luxury perfumes, with a particular focus on how Chinese university students perceive and behave towards perfumes endorsed by BLACKPINK members. This study employs a combination of textual analysis and questionnaire surveys to analyze the advertisements of four luxury perfume brands - Celine, Dior, YSL, and Tamburins- endorsed by BLACKPINK members. Through a survey of 117 Chinese university students, the relationship between the brand image conveyed by the advertisements and the consumers' brand perception is examined. The results indicate a high consistency between the visual and textual elements of the advertisements (such as the presentation style and emotional tone of the endorsers) and the perceived brand image by consumers. Moreover, the study reveals the synergistic effect of visual and textual elements in advertisements, which not only enhances the efficiency of brand information transmission but also improves consumers' overall perception and emotional connection with the brand. This study not only demonstrates the powerful influence of K-pop star endorsements but also provides important strategic insights for luxury brands in choosing endorsers and designing advertising strategies. By leveraging celebrity influence effectively, brands can achieve marketing goals and deepen the connection between consumers and the brand. Future research could further explore the relationship between different endorsers and different types of products, and how this relationship influences brand loyalty and purchasing behavior among various consumer groups.

Customers’ emotional impact on star rating and thumbs-up behavior towards food delivery service Apps

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

Media Sentiment on Air Pollution: Seasonal Trends in Relation to PM10 Levels

Air pollution remains a major public health concern globally, especially in the Western Balkan countries facing severe air quality problems. This study investigates the relationship between air quality, news media sentiment, and public discourse in Macedonia over a ten-year period (2014–2023). We employed sentiment analysis to examine the emotional tone of news coverage related to air pollution, and topic modeling to uncover recurring themes within news articles. Our analysis revealed a distinct seasonal pattern, with negative media sentiments peaking during winter months when PM10 levels were the highest. This finding aligns with the increased reliance on polluting fuels for winter heating. Interestingly, despite a stable number of neutral articles, a rise in positive-sentiment articles suggests a potential decrease in pollution levels or the effectiveness of new government policies. We identified recurring topics like air quality concerns in specific cities, public unease regarding factories, and ongoing scrutiny of government policies. Emerging topics included the impact of the COVID-19 pandemic on air quality, public discourse surrounding heating practices, and growing concerns about waste management. This study contributes to a deeper understanding of the complex interplay between air pollution data, public discourse, and media framing, offering valuable insights for policymakers and media outlets in Macedonia.

Limitations of Disembodied Computer-Generated Voice to Convey Emotion in Multimedia Lessons

The goal of this study was to determine whether learning processes and outcomes are affected by the emotional tone of the computer-generated voice in a narrated slideshow. In a between-subjects design, participants viewed a narrated slideshow on lightning formation involving a computer-generated female (Experiment 1) or male voice (Experiment 2) that displayed happy, content, angry, or sad emotion. On subsequent surveys and post-tests, students could recognize positive or negative emotions conveyed by the female voice but not the male voice. The emotional tone of the instructor’s voices had minimal impact on students’ ratings of felt emotion during learning, ratings of social connection with the instructor, or scores on retention and transfer tests of learning outcome. The findings highlight the limitations of computer-generated voices to convey emotions that trigger affective, social, and cognitive processes when an onscreen instructor is absent, thereby suggesting a boundary condition for the cognitive-affective model of e-learning.

Competence enhancement from interactive learning: Does attending conferences affect CEO turnover?

Managers can benefit from learning about stock prices and voluntary disclosure feedback to solidify their position. However, the value of managerial interactive learning is often overlooked. This paper examines the impact of CEO conference interactions on forced turnover. In specifications that include CEO fixed effects to absorb certain personality traits or work abilities, we find that directly interactive CEOs have a lower probability of turnover and weaken turnover-performance link. CEOs with interactive learning can change post-turnover profitability and curb declining performance. This entrenched CEO job after conference interactions is equally strong for first-time visitors, after excluding debriefings from other executives, where external pressures are higher, and it is much weaker for non-CEO executives, consistent with a role for CEOs' competence improvement from interactive learning. Positive and negative information content can be captured during meetings as reflected by the CEO's emotional tone and response to rumors. Our findings differ from earlier studies because of our emphasis on within-group estimation, and we focus on the impact of CEOs' interactive learning to acquire abilities rather than personality traits or job competence.

Affective music during episodic memory recollection modulates subsequent false emotional memory traces: an fMRI study.

Music is a powerful medium that influences our emotions and memories. Neuroscience research has demonstrated music's ability to engage brain regions associated with emotion, reward, motivation, and autobiographical memory. While music's role in modulating emotions has been explored extensively, our study investigates whether music can alter the emotional content of memories. Building on the theory that memories can be updated upon retrieval, we tested whether introducing emotional music during memory recollection might introduce false emotional elements into the original memory trace. We developed a 3-day episodic memory task with separate encoding, recollection, and retrieval phases. Our primary hypothesis was that emotional music played during memory recollection would increase the likelihood of introducing novel emotional components into the original memory. Behavioral findings revealed two key outcomes: 1) participants exposed to music during memory recollection were more likely to incorporate novel emotional components congruent with the paired music valence, and 2) memories retrieved 1day later exhibited a stronger emotional tone than the original memory, congruent with the valence of the music paired during the previous day's recollection. Furthermore, fMRI results revealed altered neural engagement during story recollection with music, including the amygdala, anterior hippocampus, and inferior parietal lobule. Enhanced connectivity between the amygdala and other brain regions, including the frontal and visual cortex, was observed during recollection with music, potentially contributing to more emotionally charged story reconstructions. These findings illuminate the interplay between music, emotion, and memory, offering insights into the consequences of infusing emotional music into memory recollection processes.

Illness and narrative identity: examining past and future life story chapters in individuals with bipolar disorder, diabetes mellitus or no chronic illness

ABSTRACT The purpose of this study was to investigate whether narrative identity challenges are specific to Bipolar Disorder (BD) as a mental illness or a reflection of living with chronic illness. Nineteen individuals diagnosed with BD, 29 individuals diagnosed with Type 1 Diabetes Mellitus (T1DM) and 25 controls without chronic mental or somatic illness identified past and future life story chapters which were self-rated on emotional tone and self-event connections and content-coded for agency and communion themes. Individuals with BD self-rated their past chapters as more negative and less positive, and their chapters were lower on content-coded agency and communion themes compared to T1DM and controls. There were fewer group differences for future chapters, but BD was associated with lower self-rated positive emotional tone and self-stability connections as well as lower content-coded agency and communion themes. The results indicate that narrative identity is affected in individuals with BD above and beyond the consequences of living with chronic illness. This may reflect distinct effects of mental versus somatic illness on narrative identity.

DEVELOPMENT PERSPECTIVES AND INNOVATIVE AREAS OF ARTIFICIAL INTELLIGENCE APPLICATION IN MARKETING AND PR

This article focuses on the future development and innovative uses of AI in marketing and public relations. The purpose of the article is to systematise information on new approaches to marketing and PR that use artificial intelligence. Research methods include analysis of market trends, data synthesis and case studies. The findings highlight that artificial intelligence is playing a central role in the evolution of marketing and public relations, significantly improving the efficiency of data processing, brand positioning and personalisation of communication. The study examines the contribution of tools such as Jasper and ChatGPT to marketing copywriting and content planning, as well as Frace and Fireflies to monitoring the emotional tone of brands on social media and automating meetings. Such tools as Deepl and Grammarly are highlighted to ensure translation and proofreading accuracy, as well as the use of Midjourney for creative visualisation. The analysis of the market for the use of AI in marketing indicates that the AI market in marketing and PR could reach 107.57 billion USD by 2028. However, 37% of companies are still not ready to fully implement AI due to a lack of skilled skills and resources, which highlights the need for educational initiatives to improve professional competence. The document points to the potential for revolutionary changes in the way businesses do business, including increased efficiency, security and adaptation to consumer preferences, with a focus on strategic understanding and adaptation to changing market conditions. The practical significance of the study is to provide businesses with recommendations on the effective use of AI, which can increase the competitiveness of companies. The scientific novelty of this study lies in a comprehensive analysis of the impact of artificial intelligence on various aspects of marketing and public relations, as well as in identifying the potential of automation and personalisation in customer interaction. The main barriers to the full implementation of AI in business processes are identified and strategies to overcome them are developed. Based on the research, recommendations will be formulated for the further development of innovative technologies in marketing and PR, with a focus on educational initiatives that can significantly increase the level of professional qualification in this field.

Sentiment Analysis and Topic Classification with LSTM Networks and TextRazor

In the ever-evolving landscape of social media, where user-generated content shapes digital discourse, the need for nuanced sentiment analysis and topic extraction is paramount. This paper presents a comprehensive approach utilizing advanced Natural Language Processing (NLP) techniques to enhance user experience and foster a healthier digital environment. Leveraging Long Short-Term Memory (LSTM) networks for sentiment analysis and TextRazor for topic extraction, the system provides insights into emotional tones and key themes within social media discussions. Through intuitive visualizations, users gain awareness of sentiment trends and topic distributions, empowering informed engagement. The results demonstrate high accuracy in sentiment classification with 86% and effective topic identification, contributing to the mitigation of misinformation and negativity online. This research underscores the potential of advanced NLP methods in cultivating constructive digital spaces and sets the stage for further innovation in the field.

Comparing patient education tools for chronic pain medications: Artificial intelligence chatbot versus traditional patient information leaflets.

Artificial intelligence (AI) chatbots like Conversational Generative Pre-trained Transformer (ChatGPT) have recently created much buzz, especially regarding patient education. Such informed patients understand and adhere to the management and get involved in shared decision making. The accuracy and understandability of the generated educational material are prime concerns. Thus, we compared ChatGPT with traditional patient information leaflets (PILs) about chronic pain medications. Patients' frequently asked questions were generated from PILs available on the official websites of the British Pain Society (BPS) and the Faculty of Pain Medicine. Eight blinded annexures were prepared for evaluation, consisting of traditional PILs from the BPS and AI-generated patient information materials structured similar to PILs by ChatGPT. The authors performed a comparative analysis to assess materials' readability, emotional tone, accuracy, actionability, and understandability. Readability was measured using Flesch Reading Ease (FRE), Gunning Fog Index (GFI), and Flesch-Kincaid Grade Level (FKGL). Sentiment analysis determined emotional tone. An expert panel evaluated accuracy and completeness. Actionability and understandability were assessed with the Patient Education Materials Assessment Tool. Traditional PILs generally exhibited higher readability (P values < 0.05), with [mean (standard deviation)] FRE [62.25 (1.6) versus 48 (3.7)], GFI [11.85 (0.9) versus 13.65 (0.7)], and FKGL [8.33 (0.5) versus 10.23 (0.5)] but varied emotional tones, often negative, compared to more positive sentiments in ChatGPT-generated texts. Accuracy and completeness did not significantly differ between the two. Actionability and understandability scores were comparable. While AI chatbots offer efficient information delivery, ensuring accuracy and readability, patient-centeredness remains crucial. It is imperative to balance innovation with evidence-based practice.

Understanding first aid knowledge adoption on social media with an extended information adoption model

PurposeSocial media provides a convenient way to popularise first aid knowledge amongst the general public. So far, little is known about the factors influencing individuals’ adoption of first aid knowledge on social media. Drawing on the information adoption model (IAM), this study investigates the joint effects of cognitive factors (e.g. perceived information usefulness (PIU)), affective factors (e.g. arousal (AR)) and social factors (e.g. descriptive norms (DN)) on first aid knowledge adoption (KA) and examines their antecedent cues from the perspective of information characteristics.Design/methodology/approachThe data were collected from 375 social media users, and the structural equation model was adopted to analyse the results.FindingsThe results indicate that PIU, AR and DN all have positive direct effects on first aid KA. Additionally, the study highlights the positive synergistic effect of AR and PIU. Furthermore, the study suggests that AR is determined by message vividness (MV) and emotional tone (ET), whilst DN are determined by peer endorsement (PEE) and expert endorsement (EXE).Originality/valueOur research is groundbreaking as it delves into the adoption of first aid knowledge through social media, thus pushing the boundaries of existing information adoption literature. Additionally, our study enhances the IAM by incorporating emotional and social elements and provides valuable insights for promoting the spread of first aid knowledge via social media.

Effects of conflicting emotional cues on toddlers' emotion perception.

The communication of emotion is dynamic and occurs across multiple channels, such as facial expression and tone of voice. When cues are in conflict, interpreting emotion can become challenging. Here, we examined the effects of incongruent emotional cues on toddlers' interpretation of emotions. We presented 33 children (22-26 months, Mage = 23.8 months, 15 female) with side-by-side images of faces along with sentences spoken in a tone of voice that conflicted with semantic content. One of the two faces matched the emotional tone of the audio, whereas the other matched the semantic content. For both congruent and incongruent trials, toddlers showed no overall looking preference to either type of face stimuli. However, during the second exposure to the sentences of incongruent trials, older children tended to look longer to the face matching semantic content when listening to happy vs. angry content. Results inform our understanding of the early development of complex emotion understanding.

AI-Assisted Sentiment Analysis of ACFAS Fellowship-Trained Foot and Ankle Surgeon Online Reviews

Online physician ratings and reviews prove useful among patients when selecting a provider. Analyzing such reviews across medical and surgical specialties to determine their emotional tone through sentiment analysis yielded varying levels of positivity, negativity, and neutrality. To provide insight into what patients are saying, this study similarly analyzes the sentiment of physician ratings and reviews among foot and ankle surgeons. Healthgrades ratings and reviews, entered as of February 2024, were collected among the American College of Foot and Ankle Surgeons (ACFAS) fellows along with each surgeon's demographic information. ChatGPT was used to perform a sentiment analysis to describe the positivity, negativity, and neutrality of online physician reviews. Ratings and review sentiment were described among the sample and between sexes. Among 268 fellows, men received higher average rating scores than women (p = 0.02), From the 2,339 reviews, women received a greater proportion of negative reviews compared to men (p<0.001). The overall sentiment scores among men were higher than women (p < 0.001). There existed a very weak inverse relationship between ratings and years in practice (R = -0.16; p = 0.01). Fellowship-trained foot and ankle surgeons received predominantly positive reviews. When comparing sexes, males received higher ratings with higher sentiment scores. As patients place high credence in online reviews when selecting their provider, surgeons should remain mindful of and monitor or manage their online reputation. Level of EvidenceIV

Generative AI Generating Buzz: Volume, Engagement, and Content of Initial Reactions to ChatGPT in Discussions Across Education-Related Subreddits

The emergence of generative artificial intelligence (GenAI) has ignited intense debates regarding its potential benefits and detriments for education. Despite such widespread discussions, insights into GenAI's impact have often been limited to anecdotal evidence and a narrow scope of educational stakeholders. To understand the broader response to GenAI tools in education, we collected and analyzed public online discussions of ChatGPT, a leading GenAI tool, in the first four months following the tool’s release. We collected 345 posts and 6,463 comments about ChatGPT from 25 education-focused subreddits. We analyzed the volume, engagement, and content of ChatGPT discussions through descriptive statistics and natural language processing techniques. Findings show relatively low volume of ChatGPT discussions, unevenly spread across education-related subreddits—with the majority of the discussions occurring in two subreddits while six subreddits did not have any discussions. Despite this, the level of engagement within ChatGPT posts was substantial; for instance, a ChatGPT post hosted a median of 15 comments, and these comments were lengthy, indicating rich engagement rather than superficial. The content of ChatGPT discussions across the six largest education-related subreddits differed in the degree of analytical thinking and emotional tone even while sharing a predominant focus on students and AI. Diverse reactions to and perspectives on GenAI—observed from varied levels of volume, engagement, and content of ChatGPT across educational-related subreddits—highlights how diverse educational stakeholders reacted to GenAI differently, offering insights into how to explore, analyze, comprehend the spread and adoption of technological innovation in education.

Do words matter: Investigating the association between linguistic features of accounting examinations and marks

The use of linguistic features in school assessments, as well as the impact of these factors on the outcome of assessments have received limited attention in the literature. With this study I aimed to analyse linguistic features of accounting examinations. A quantitative technique, using tests for correlation, was employed to analyse the Grade 12 National Senior Certificate accounting examinations from 2011 until 2021. Advanced textual analysis software was used to identify connections between specific linguistic features and the marks obtained in these examinations. The findings of this study suggest that increasing the levels of linguistic features associated with analytical thinking, emotional tone and big words, which may be assumed to promote comprehension, may in reality be counterproductive, consequently potentially resulting in poorer marks attained in assessments in the future. The findings of this study are important for the creators of assessment to consider when developing accounting assessment. Given the effect of linguistic features on assessment results as identified in this study, it contributes to the debate on the use of certain linguistic features in assessment.

End-of-life Care Patient Information Leaflets-A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini.

End-of-life care (EOLC) is a critical aspect of healthcare, yet accessing reliable information remains challenging, particularly in culturally diverse contexts like India. This study investigates the potential of artificial intelligence (AI) in addressing the informational gap by analyzing patient information leaflets (PILs) generated by AI chatbots on EOLC. Using a comparative research design, PILs generated by ChatGPT and Google Gemini were evaluated for readability, sentiment, accuracy, completeness, and suitability. Readability was assessed using established metrics, sentiment analysis determined emotional tone, accuracy, and completeness were rated by subject experts, and suitability was evaluated using the Patient Education Materials Assessment Tool (PEMAT). Google Gemini PILs exhibited superior readability and actionability compared to ChatGPT PILs. Both conveyed positive sentiments and high levels of accuracy and completeness, with Google Gemini PILs showing slightly lower accuracy scores. The findings highlight the promising role of AI in enhancing patient education in EOLC, with implications for improving care outcomes and promoting informed decision-making in diverse cultural settings. Ongoing refinement and innovation in AI-driven patient education strategies are needed to ensure compassionate and culturally sensitive EOLC. Gondode PG, Khanna P, Sharma P, Duggal S, Garg N. End-of-life Care Patient Information Leaflets-A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini. Indian J Crit Care Med 2024;28(6):561-568.

A multimodal analysis of the English-to-Arabic adaptation of Disney’s “The Bare Necessities”

Abstract Due to the diverse roles that songs play in films and the growing need for movie distribution in various languages and cultures, the challenge of translating film songs from one language to another has become formidable. This research aims to apply the multimodal three-dimensional model proposed by Cui and Wang (2022. Film song translation: Verbal, vocal, and visual dimensions: On the Chinese translation of Amazing Grace in the film Forever Young. Babel 68(4). 565–585) to analyze the Arabic dubbed version of the 2016 film song “The Bare Necessities.” The analysis revealed that, verbally, the Arabic dubbed version of the song maintained the gay emotional tone and length but made adaptations to the information, metaphors, and images used in the source. Vocally, similar to the English source song, the Arabic dubbed version did not adhere to 1 m and has a 4/4 time signature, aligning with the catchy melody and reinforcing the song’s theme of embracing simplicity and savoring life’s essentials effortlessly. Finally, visually, the analysis showed that the Arabic dubbed version matches the plot, characters and background pictures on the screen. The study contributed by incorporating a visual analysis of songs, adding camera elements, namely camera movement and shot size, to gain a better understanding of the scenes under analysis. The use of over-the-shoulder shots created a sense of conversation that complements the duet, and the pan right and left during dancing added a vividness that matches the verbal gay tone and vocal lightness of the song. This finding, considering visual camera elements, can be regarded as a suggested revision to the original multimodal framework introduced by Cui and Wang. Further implications are discussed.

Emotional Intelligence in Text-To-Speech Synthesis in Pali Language Using Fuzzy Logic

The field of emotional text-to-speech (TTS) synthesis is making swift progress within the realm of artificial intelligence, holding immense promise to transform our interaction with technology. By using advanced algorithms to analyze and understand the emotional content of text, these systems are able to produce spoken language that accurately conveys the intended emotional tone of the message. Despite the existence of several Text-To-Speech systems across various languages, Pali language is yet to have its own. As a result, we have taken the initiative to create a Text-To-Speech synthesizer exclusively for Pali. Our system offers an end-to-end solution for emotional speech synthesis via Text-To-Speech. We address the problem by incorporating disentangled, well-grained prosody features with global, sentence-level emotion implanting. These well-grained features learn to denote local prosodic differences disentangled from the speaker, tone, and worldwide emotion label. Prosody is usually modeled by rules, so we have implemented the fuzzy logic system to develop a controller for the prosody of Pali speech. The fuzzy controller handles different linguistic parameters in three types of sentences: interrogative, exclamatory, and declarative. The final system produces comprehensible speech that mimics the appropriate intonation for every type of sentence. In this paper, we introduce and outline the application of a fuzzy paradigm to incorporate a Text-To-Speech system for the Pali language while preserving a rule-based Concatenative synthesizer. In the outline of classic Concatenative TTS systems, we recommend a new method in order to increase Concatenative unit selection computation, directed at increasing synthetic speech perceptual superiority. In order to tackle the problem of phonemes that are prone to multiple descriptions in rule-based speech synthesis, the proposed solution involves a fuzzy system. In the introductory section, we offer a concise description of the current context surrounding the challenge of emotional speech synthesis. The second section of this paper outlines the notable advancements made in emotional speech synthesis, acknowledging the contributions of various researchers in this field. The third section delves into the technical details of implementing a fuzzy system The last section of the paper presents the main conclusions and future research scope.