This study reviews recent progress in sarcasm detection, with a particular emphasis on audio-based methods.

Drawing on 58 scholarly articles, it traces the development of machine learning, deep learning, and hybrid

approaches designed to identify sarcasm through vocal features such as intonation, pitch, and rhythm. The

review underscores the need for robust models capable of capturing cultural and linguistic variations in how

Sarcasm detection, particularly in audio signals, represents a critical challenge in the field of computational

linguistics, deep learning, and signal processing. Unlike traditional textual sarcasm detection, detecting

sarcasm in audio involves analyzing vocal cues such as pitch, tone, prosody, and intonation, which makes it an

even more complex task. The intersection of deep learning and signal processing has enabled researchers to

develop sophisticated methods to understand these intricate vocal patterns and detect sarcasm with increasing

accuracy.

Sarcasm often conveys emotions and meanings that are opposite to what is spoken. It can create ambiguity and

misunderstanding in human-computer interaction systems, virtual assistants, or even sentiment analysis tools.

Previous studies have made strides in using various signal processing techniques to detect anomalies or

specific patterns within data. For example, anomaly detection algorithms have been applied in autonomous

vehicles (Bello-Salau et al., 2018) and motor systems (Chen et al., 2024), proving that identifying outliers

within complex data sets is feasible with the right techniques. This same approach can be adapted to detect

vocal anomalies such as sarcasm in audio signals. Signal processing methods have also been applied in real

estate valuations, where deep learning models leverage multiple modalities to enhance accuracy (Despotovic et

al., 2023). These advancements demonstrate the effectiveness of combining deep learning with supplementary

modalities for tasks requiring pattern recognition, further justifying its application in detecting sarcasm in

speech.

Another area that demonstrates the importance of detecting nuanced vocal signals is the evaluation of acoustic

sources. Studies on direction-finding techniques of acoustic sources using uniform linear arrays (Uddin et al.,

2021a; 2021b) showcase the feasibility of using advanced signal processing for tasks that require precise audio

interpretation. Similarly, the ability to detect sarcasm in speech relies on extracting and processing vocal

signals, akin to detecting the origin of sound in an acoustic array.

In conclusion, the combination of deep learning and signal processing holds great potential in enhancing the

accuracy of sarcasm detection in audio signals. With existing methodologies demonstrating the efficacy of

these approaches in related domains, the continued development of sarcasm detection technologies could

The field of sarcasm detection, particularly in online comments and audio signals, has garnered significant

attention due to the complex nature of sarcasm as a form of communication. Detecting sarcasm has proven to

be particularly challenging, requiring not only an understanding of linguistic structures but also an appreciation

of the context, tone, and underlying intent of the message. Research has increasingly turned to machine

learning and deep learning techniques, combined with signal processing, to address this complexity.

techniques. They highlight the growing importance of sarcasm detection in online interactions, particularly on

social media platforms, where sarcastic remarks can distort the sentiment of user-generated content. The study

emphasizes the importance of contextual and linguistic features in improving the accuracy of machine learning

At the same time, Xin et al. (2024) focus on noise reduction and data mining techniques, which are critical in

ensuring the reliability of signal processing, particularly in dynamic environments like pavement response

signals. Though their work is not directly related to sarcasm detection, their research on noise reduction offers

valuable insights into improving the clarity of audio data, a crucial step when detecting sarcasm in spoken

language. Sarcasm often relies on vocal cues such as intonation and pitch, and ensuring that these audio signals

are clear and interpretable is key to improving detection accuracy. Therefore, their work provides foundational

methods that can be adapted to audio-based sarcasm detection systems.

Emotion analysis is another key area closely linked to sarcasm detection. BagićBabac (2023) explores the role

of emotion in user reactions to online news. Emotion analysis plays a crucial role in identifying sarcastic tones,

as sarcasm is often laden with emotional undercurrents like frustration, humor, or disdain. By understanding

Beyond sarcasm detection, the growing concern about datafication in the workplace highlights another

important dimension in the collection and analysis of communication data. Rigamonti et al. (2024) investigate

In the realm of public discourse, argumentation and sarcasm frequently intersect, particularly on contentious

topics like climate change. Foderaro and Lorentzen (2023) examine argumentative practices and patterns in

climate change debates on Twitter, showing how sarcasm is often used to belittle or challenge opposing

viewpoints. In these debates, sarcasm can either strengthen an argument by undermining an opponent's stance

or confuse the debate by injecting ambiguity into the conversation. Understanding these patterns of sarcasm

use is essential for developing more accurate detection systems, especially in public discourse settings where

sarcasm is used strategically.

Another crucial consideration in sarcasm detection is the socio-cultural context, particularly in how different

The literature reveals a growing convergence of machine learning, emotion analysis, signal processing, and

socio-cultural considerations in the detection of sarcasm. Šandor and BagićBabac (2024) demonstrate how

machine learning models can effectively capture sarcasm in online comments, while Xin et al. (2024) provide

insights into noise reduction techniques crucial for detecting vocal sarcasm. Emotion analysis, as explored by

Rigamonti et al. (2024) remind us of the ethical concerns surrounding data collection, which are equally

pertinent in the realm of sarcasm detection. Finally, Foderaro and Lorentzen (2023) and AlRowais and Alsaeed

(2023) illustrate how sarcasm operates in both public discourse and different linguistic contexts, necessitating

adaptable and culturally aware detection systems.

Several studies have underscored the complexity of sarcasm detection in audio signals, focusing on the

acoustic and prosodic features that differentiate sarcastic speech from literal speech. Early research

concentrated on traditional signal processing techniques, such as pitch, tone, and speech rate analysis, to

identify sarcasm (Zhang & Luo, 2020). These methods, although insightful, were limited in capturing the full

range of vocal cues due to their reliance on manual feature extraction.

The introduction of deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent

Neural Networks (RNNs), has significantly improved the field. These models, particularly when combined

with signal processing techniques like Mel-frequency cepstral coefficients (MFCCs) and spectrogram analysis,

have demonstrated greater accuracy in capturing the intricate patterns of sarcasm. For instance, Sharma et al.

have emerged as an effective solution for detecting sarcasm. Giuggioli et al. (2024) highlight the application of

multimodal systems that integrate both audio and textual cues, enabling a more comprehensive analysis of

sarcastic speech, researchers have gained deeper insights into how sarcasm is processed in the brain, offering

valuable data that can inform and refine computational models. This interdisciplinary approach has the

potential to improve model accuracy by providing a cognitive framework for understanding sarcasm as a

complex social and emotional phenomenon.

The rise of deepfake technologies and their potential to simulate sarcastic speech poses both challenges and

opportunities for sarcasm detection systems (Lyu, 2024). While deepfakes can obscure authentic vocal signals,

they also provide a testing ground for refining sarcasm detection models by exposing them to manipulated

speech data, pushing the boundaries of AI’s capability in discerning genuine from fabricated sarcasm.

As sarcasm continues to play a prominent role in human communication, especially in social media and

The integration of technology in leadership and education has garnered significant attention in recent research.

Ann and Aziz (2022) explored the intersection of avatars and face-to-face learning, presenting a thematic

analysis of East African perspectives on online leadership education. Their findings indicate that digital

environments can enhance leadership learning by providing unique opportunities for interaction and

engagement among participants. Similarly, the work of ArthanarisamyRamaswamy and Palaniswamy (2022)

contributes to this technological discourse by investigating emotion recognition through EEG and

physiological signals. Their comparative study highlights the effectiveness of various methods in accurately

recognizing emotions, which could enhance user experience in virtual learning platforms.

opinion, which could be leveraged in educational settings to gauge student sentiment and engagement.

Further exploring machine learning applications, Khan et al. (2022) conducted a systematic analysis of various

classifiers for predicting dementia. Their study emphasizes the need for robust predictive models in healthcare,

thereby highlighting the potential for machine learning techniques to be employed in training health

professionals, fostering a deeper understanding of patient care dynamics. In the marketing domain, Lappeman

et al. (2022) examined social media sentiment to uncover the reasons behind customer churn. Their findings

indicate that analyzing customer feedback can inform business strategies, thus contributing to the discourse on

customer relationship management.

narratives significantly influence employees' perceptions and engagement levels, reinforcing the importance of

effective communication in organizational settings.

revealing how different age groups engage with technology in romantic contexts. Sibanda et al. (2021)

presented a methodology for designing a reconfigurable guillotine shear and bending press machine,

illustrating the convergence of engineering and technology in industrial applications. Meanwhile, Tharwat

(2021) introduced independent component analysis as a powerful tool for data processing, emphasizing its

relevance in various research fields, including signal processing and machine learning.

Travassos et al. (2021) reviewed the application of artificial neural networks and machine learning techniques

to Ground Penetrating Radar, indicating a growing interest in combining traditional engineering practices with

modern computational methods. Jiang et al. (2020) and Rantanen et al. (2020) further reinforced this trend by

presenting studies on vehicle ego-localization and online corporate reputation classification, respectively.

need for interdisciplinary approaches to leverage the full potential of emerging technologies in education,

healthcare, marketing, and engineering.

In this review paper, a systematic methodology was employed to analyze existing literature related to the

impact of artificial intelligence on various sectors. Initially, a comprehensive search was conducted to identify

relevant studies from reputed academic journals. The search included a variety of databases to ensure a diverse

collection of articles that cover multiple aspects of artificial intelligence, such as its applications in education,

healthcare, marketing, and engineering. The initial selection yielded a total of 75 papers that were deemed

relevant to the research topic. Each paper was meticulously reviewed based on predefined inclusion criteria,

which focused on the relevance, quality, and contribution of the studies to the existing body of knowledge. The

analysis, allowing for the identification of key trends, gaps, and implications for future research in the field of

artificial intelligence.

The primary objective of this review paper is to synthesize the current body of knowledge regarding the impact

of artificial intelligence across various sectors, specifically to evaluate the applications of AI in different fields

and analyze the trends observed in the literature.