Speech Recognition Language Research Articles

Exploring the Implementation Methods of AI-Driven Robots in Media Interaction Experience Design

The purpose of this study is to deeply explore the innovative implementation methods of artificial intelligence (AI)-driven robots in media interactive experience design. Firstly, this paper expounds how AI-driven robot technology can provide users with a more natural and intelligent interactive experience through natural language processing (NLP), speech recognition, computer vision and other technologies, thus enhancing users' sense of participation and immersion. In order to realize the efficient application of AI-driven robot in media interaction, this study proposes a series of implementation methods, including improving the accuracy and personalization of robot interaction by using NLP and deep learning algorithm; Identify user expressions and gestures through computer vision technology to enhance the naturalness of interaction; Multi-modal interactive technology is used to integrate multi-sensory channel information to provide comprehensive interactive experience; Use deep learning technology to train the robot's ability to perceive and understand users' needs, and introduce emotion analysis technology to realize more humanized service; By constructing user portraits, we design personalized interaction schemes that adapt to different scenarios and users. In the experimental part, three different scenes, library, shopping mall and museum, are selected to verify the effectiveness of the design scheme. The results show that the AI-driven robot significantly improves the user's satisfaction and emotional experience under the personalized interaction scheme.

Review of Convolutional Neural Network

Over the past decade, the domain of deep learning has emerged as a swiftly expanding area of interest among researchers globally. It offers substantial benefits over traditional shallow networks, particularly in the realms of feature extraction and model fitting. Deep learning excels at uncovering intricate, distributed features from raw data, which exhibit robust generalization capabilities. It has triumphantly addressed challenges that were once deemed intractable within the field of artificial intelligence. With the exponential growth in the volume of training data and a marked increase in computational power, deep learning has achieved remarkable strides and found applications across a spectrum of domains, including but not limited to object detection, computer vision, natural language processing, speech recognition, and semantic parsing, thereby accelerating the evolution of artificial intelligence. Deep learning encompasses a series of non-linear transformations organized hierarchically, with deep neural networks representing the predominant form of contemporary deep learning methodologies. Inspired by the neural connections found in the animal visual cortex, these networks are characterized by local connectivity, shared weights, and pooling operations. Such attributes not only simplify the network model and curtail the number of trainable parameters but also engender invariance to shifts, distortions, and scaling, thereby endowing the model with enhanced robustness and fault tolerance. This makes the training and optimization of the network structure more manageable. This paper commences with a retrospective on the evolution of convolutional neural networks (CNNs). Subsequently, it delineates the architectures of neuron models and multilayer perceptron. It proceeds to dissect the CNN architecture, which typically encompasses a sequence of convolutional and pooling layers, culminating in fully connected layers, each serving distinct functions. The paper also elaborates on various enhanced algorithms for CNNs, such as the “Network in Network” and “Spatial Transformer Networks,” while also introducing supervised and unsupervised learning methodologies pertinent to CNNs and highlighting several prevalent open-source tools. Further, the paper scrutinizes the application of CNNs in various fields, including image classification, facial recognition, audio retrieval, electrocardiogram analysis, and object detection. It posits the amalgamation of CNNs with recurrent neural networks as a potential alternative for training datasets. The research culminates in the design of CNN structures with varying parameters and depths, supported by experimental analysis that elucidates the interplay among these parameters and the ramifications of their configuration. Ultimately, the paper synthesizes the merits and lingering challenges associated with CNNs and their practical applications.

A Greek Conversational Agent for Hematologic Malignancies: Usability and User Experience Assessment.

Enabling patients to actively document their health information significantly improves understanding of how therapies work, disease progression, and overall life quality affects for those living with chronic disorders such as hematologic malignancies. Advancements in artificial intelligence, particularly in areas such as natural language processing and speech recognition, have resulted in the development of interactive tools tailored for healthcare. This paper introduces an innovative conversational agent tailored to the Greek language. The design and deployment of this tool, which incorporates sentiment analysis, aims at gathering detailed family histories and symptom data from individuals diagnosed with hematologic malignancies. Furthermore, we discuss the preliminary findings from a feasibility study assessing the tool's effectiveness. Initial feedback on the user experience suggests a positive reception towards the agent's usability, highlighting its potential to enhance patient engagement in a clinical setting.

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

Enhancing customer experience through AI-driven language processing in service interactions

In today’s competitive service industries, delivering exceptional customer experiences is paramount. This paper explores how AI-driven language processing technologies enhance customer service interactions. Key technologies such as Natural Language Processing (NLP), chatbots, virtual assistants, speech recognition, and machine learning are crucial in transforming customer service. These technologies enable personalized, efficient, and consistent customer interactions, significantly improving overall customer satisfaction. However, implementing these technologies has challenges, including data quality, system integration, privacy concerns, user acceptance, and financial investment. Addressing these challenges is essential for successful AI adoption. Future trends indicate further advancements in AI capabilities, integration with IoT and blockchain technologies, and evolving ethical and regulatory considerations. The long-term impact of AI on service industries promises more efficient operations, cost savings, and superior customer experiences, giving companies a competitive advantage in the market. This paper provides insights into leveraging AI to enhance customer experience, outlining the benefits and challenges while speculating on future directions and implications.

Implementation of Data Mining for Speech Recognition Classification of Sundanese Dialect Using KNN Method with MFCC Feature Extraction

The importance of preservation and development of speech recognition technology for regional languages such as Sundanese, which have unique phonetic characteristics. Regional language speech recognition can assist in the development of local, educational, and cultural preservation applications to implement and evaluate the effectiveness of the combination of MFCC and KNN methods in classifying Sundanese dialect speech recognition. Methods used include trait extraction with MFCC, which converts voice data into numerical representations based on frequency characteristics, and classification with KNN, which groups data based on similarity to train data. The Dataset used consisted of speech recordings of Western and Southern Sundanese dialects. The results showed that the k-Nearest Neighbors (KNN) method can classify Sundanese dialect speech recognition with an accuracy of 80.00%, showing good ability in distinguishing "Western" and "southern" dialects. Mel-Frequency Cepstral Coefficients (MFCC) proved to be very effective in extracting sound features, helping KNN achieve low error rates. The combination of MFCC and KNN proved effective for speech recognition classification of Sundanese dialects, providing satisfactory results with high accuracy.

Regional Language Speech Recognition from Bone Conducted Speech Signals Through CCWT Algorithm

Speech enhancement, or SE, is a method of converting an input speech signal into a target signal with improved quality of voice and readability. To hear the voice, the skeleton bone vibrates ultra smooth thanks to bone conduction. The benefits of Bone-Conducted Microphone (BCM) speech include noise reduction and enhanced communication quality in high-noise environments. To acquire signals and precisely model word phonemes, BCM relies on the placement of bones. Certain computer techniques are expensive and ineffective in simulating signal phonemes. Three wavelet transform techniques are presented in this work: complex continuous wavelet transforms (CCWT), steady wavelet transforms (SWT), and discrete wavelet transforms (DWT). The right ramp, the voice box, and the mastoid were the three distinct bony locations for which the speech intelligibility of the BCM signal was evaluated. The listener evaluated the comprehension of the speech after obtaining the BCM signal for Tamil words. Speech quality is enhanced by the location of the larynx bone in comparison to alternative calculation methods.

Speaking Practice using ChatGPT’s Voice Conversation: A Review on Potentials and Concerns

Investigating the pedagogical potentials and concerns of ChatGPT's voice conversation feature within the context of language learning, this study begins by exploring the historical context and limitations of traditional language learning methods, such as language exchange, conversation classes, and role-plays. The discussion then shifts to the rise of AI in education, highlighting ChatGPT's advanced capabilities in providing personalized, interactive, and efficient language instruction through natural language processing, machine learning, and speech recognition. While offering advantages such as accessibility, adaptability, and personalized feedback, the technology also presents limitations in accurately assessing pronunciation and contextual nuances. To effectively integrate ChatGPT into language instruction, educators must carefully consider its strengths and weaknesses. This research explores those things to optimize their use and ensure they complement, rather than replace, traditional language teaching methods.

BUILDING A SCALABLE DATASET FOR FRIDAY SERMONS OF AUDIO AND TEXT (SAT)

Context. Today, collecting and creating datasets in various sectors has become increasingly prevalent. Despite this widespread data production, a gap still exists in specialized domains, particularly in the Islamic Friday Sermons (IFS) domain. It is rich with theological, cultural, and linguistic studies that are relevant to Arab and Muslim countries, not just religious discourses. Objective. The goal of this research is to bridge this lack by introducing a comprehensive Sermon Audio and Text (SAT) dataset with its metadata. It seeks to provide an extensive resource for religion, linguistics, and sociology studies. Moreover, it aims to support advancements in Artificial Intelligence (AI), such as Natural Language Processing and Speech Recognition technologies. Method. The development of the SAT dataset was conducted through four distinct phases: planning, creation and processing, measurement, and deployment. The SAT dataset contains a collection of 21,253 audio and corresponding transcript files that were successfully created. Advanced audio processing techniques were used to enhance speech recognition and provide a dataset that is suitable for wide-range use. Results. The fine-tuned SAT dataset achieved a 5.13% Word Error Rate (WER), indicating a significant improvement in accuracy compared to the baseline model of Microsoft Azure Speech. This achievement indicates the dataset’s quality and the employed processing techniques’ effectiveness. In light of this, a novel Closest Matching Phrase (CMP) algorithm was developed to enhance the high confidence of equivalent speech-to-text by adjusting lower ratio phrases. Conclusions. This research contributes significant impact and insight into different studies, such as religion, linguistics, and sociology, providing invaluable insights and resources. In addition, it is demonstrating its potential in Artificial Intelligence (AI) and supporting its applications. In future research, we will focus on enriching this dataset expansion by adding a sign language video corpus, using advanced alignment techniques. It will support ongoing Machine Translation (MT) developments for a broader understanding of Islamic Friday Sermons across different linguistics and cultures.

CAM: A cross-lingual adaptation framework for low-resource language speech recognition

In this paper, a novel cross-lingual adaptation framework called CAM is presented for low-resource language speech recognition (LLSR). It is based on the recent popular adapter method. CAM is achieved by adapting self-supervised speech models (SSMs) from source languages to target low-resource languages in a two-stage process. CAM fills two research gaps existing in current methods: (i) language similarity is not effectively considered; (ii) the performance-efficiency trade-off is not well balanced. Specifically, two key components, a similarity-aware fusion module (SAFM) and an adapter weight-sharing strategy (AWSS), are designed. A well-trained adapter is introduced by SAFM to compute precise language similarities via dot product. AWSS takes both performance and efficiency into consideration by sharing adapter weights. Experimental results on two corpora, FLEURS and Common Voice, demonstrate that CAM equipped with these two designs, denoted as performance-oriented CAM (P-CAM), obtains state-of-the-art (SOTA) performance with satisfactory efficiency compared to current leading methods. Besides, an efficiency-oriented CAM (E-CAM) by introducing a weight-space fusion module (WSFM) is presented. The core of WSFM is to average the weights of multiple adapters into a new adapter. Compared to full fine-tuning, only 5.0% of trainable parameters are required for E-CAM while a 2.3% relative average word error rate (WER) reduction is obtained. CAM brings a performance- or efficiency-oriented trade-off to meet the different needs of speech recognition systems.

Exploring Sentiment Analysis across Text, Audio, and Video: A Comprehensive Approach and Future Directions

Abstract: This study presents a comprehensive exploration of sentiment analysis techniques across text, audio, and video modalities. Leveraging natural language processing (NLP), speech recognition, and computer vision algorithms, the research demonstrates the versatility and adaptability of sentiment analysis across diverse data sources. The necessity of such an approach lies in its ability to provide deeper insights into user emotions and opinions expressed in various mediums, including written text, spoken language, and visual content. Moreover, the study highlights the importance of sentiment analysis in understanding customer feedback, market trends, social media sentiments, and sentiment-aware recommendation systems. Future directions include advancing algorithmic accuracy and efficiency, integrating multimodal fusion techniques, and exploring applications in diverse domains, thereby paving the way for enhanced sentiment analysis capabilities and broader realworld applications

Integrated End-to-End automatic speech recognition for languages for agglutinative languages

The relevance of the problem of automatic speech recognition lies in the lack of research for low-resource languages, stemming from limited training data and the necessity for new technologies to enhance efficiency and performance. The purpose of this work was to study the main aspects of integrated end-to-end speech recognition and the use of modern technologies in the natural processing of agglutinative languages, including Kazakh. In this article, the study of language models was carried out using comparative, graphic, statistical and analytical-synthetic methods, which were used in combination. This paper addresses automatic speech recognition (ASR) in agglutinative languages, particularly Kazakh, through a unified neural network model that integrates both acoustic and language modeling. Employing advanced techniques like connectionist temporal classification and attention mechanisms, the study focuses on effective speech-to-text transcription for languages with complex morphologies. Transfer learning from high-resource languages helps mitigate data scarcity in languages such as Kazakh, Kyrgyz, Uzbek, Turkish, and Azerbaijani. The research assesses model performance, underscores ASR challenges, and proposes advancements for these languages. It includes a comparative analysis of phonetic and word-formation features in agglutinative Turkic languages, using statistical data. The findings aid further research in linguistics and technology for enhancing speech recognition and synthesis, contributing to voice identification and automation processes.

AGRICULTURAL FIELD MANAGEMENT PROBLEMS: FROM CLASSIC AGRO-TECHNOLOGIES TO ARTIFICIAL INTELLIGENCE

The text is straightforward and precise. The agricultural sector has been significantly impacted by artificial intelligence (AI) technologies such as expert systems, natural language processing, speech recognition, and machine vision. This is due to factors such as the rising global population, increasing demand for food, changing weather conditions, and water availability. AI has not only increased the quantity of work in agriculture but also improved its quality. Researchers and scientists are now adopting new IoT technologies in smart farming to enable farmers to use AI technology for the advancement of seeds, crop protection, and fertilizers. This would enhance the financial viability of farmers and contribute to the general economic growth of the nation. AI is being used in three primary areas in agriculture: soil and crop monitoring, predictive analytics, and agricultural robots. Farmers are increasingly using sensors and soil sampling to collect data for farm management systems, which will be utilized for future studies and analysis. This paper enhances the area by doing a study of artificial intelligence applications in the agriculture industry. The text provides an introduction to AI, covering several AI techniques used in the agricultural sector, such as machine learning, the Internet of Things (IoT), expert systems, image processing, and computer vision.

Interactive AI Chatbot for Mental Illness

In today's increasingly technological world, mental health awareness is paramount, with the intersection of technology and wellness offering innovative solutions to alleviation of depression symptoms, attrition, and loss of follow-up in mental health treatment. To start a good life healthcare is more important. Mental health problems becoming more common, there's a need for easy-to-access support. Our chatbot uses smart technology to understand what users are feeling, offer kind responses. It's built using advanced computer learning to get better at recognizing and dealing with different mental health concerns. In this application implement natural language processing (NLP) and speech recognition. The application includes a robust authentication system for user security, featuring traditional methods like username/password verification, alongside a graphical password authentication system. Users can select a random image during registration, which is then split into shares for secure storage. The chatbot analyzes user queries to recommend tailored stress-relief strategies, including mindfulness exercises, relaxation techniques, and personalized yoga poses displayed with accompanying images and instructions. The backend manages user data, recommendation algorithms, and integration with external services, while the frontend offers intuitive interfaces for seamless user interaction. Thorough testing ensures functionality, usability, and security before deployment, with continuous improvement based on user feedback and technological advancements driving the project evolution.

The Relationship between Interpreting Anxiety and Learning Motivation Assisted by Long Short-Term Memory Neural Network Algorithms

The Long Short-Term Memory (LSTM) neural network algorithm has achieved significant results in natural language processing and speech recognition. This study explores the application of the LSTM algorithm in the study of the relationship between interpreting anxiety and learning motivation. The article mainly uses questionnaire survey method to obtain research data. The participates include students engaged in translation research and professional translators. After statistical analysis of the research data, it was found that there was a significant correlation between translation anxiety and learning motivation (r=-0.62, p<0.001). According to calculation results, the average score is 3.42 and the standard deviation is 0.76. The average score of evaluating the learning enthusiasm of participants through corresponding questionnaires is 4.27, with an SD of 0.92. Further regression analysis of the data was conducted, and the results showed that respondents with low translation anxiety had stronger learning motivation（β=- 0.43, p<0.001). It can be seen that reducing learners' fear of translation can enhance their learning motivation, thereby stimulating their interest in learning. This indicates that reducing translation anxiety may be helpful to improve the learning motivation level of interpreting learners. The study is of great significance for improving the learning enthusiasm of translation learners and reducing translation anxiety and provides a new perspective and research method for future related research.

Artificial Intelligence

Abstract: We have chosen this topic to spotlight on one of the most technological trend these days known as AI(Artificial Intelligent). Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. AI brings up images of high- functioning, human-like robots taking over the world, AI isn’t intended to replace humans. Therefore we will discuss some of the most important aspects related to AI in which it will help in a better understanding of Artificial Intelligent and both its advantages and disadvantages to be able to protect ourselves from the upcoming technological trend.

A comparative study of machine learning-based Chinese dialect speech recognition

As the official language of China, Mandarin is used on all formal occasions. However, due to China's rich history and vast population, dialects have become the primary language spoken by many people. While not taught in school, dialects serve as unofficial languages in China and are passed down through word of mouth. In recent years, the use of dialects has significantly declined as people have adapted to using official Mandarin in all settings. However, among the elderly, dialects remain the primary language of communication. The great need to use dialects as the primary mode of communication has laid the foundation for the importance and high demand for dialect speech recognition. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, research on this topic is further analysed and compared. The results suggest that there has been a certain degree of development and progress in dialect speech recognition technology. However, further research is needed to overcome the limitations of low-resource language speech recognition.

Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey

INTRODUCTION: This piece of work provides the description of integrated intelligent computing models for the interpretation of cognitive-based neurological diseases in children. These diseases can have a significant impact on children's cognitive and developmental functioning. 
 OBJECTIVES: The research work review the current diagnosis and treatment methods for cognitive based neurological diseases and discusses the potential of machine learning, deep learning, Natural language processing, speech recognition, brain imaging, and signal processing techniques in interpreting the diseases.
 METHODS: A survey of recent research on integrated intelligent computing models for cognitive-based neurological disease interpretation in children is presented, highlighting the benefits and limitations of these models.
 RESULTS: The significant of this work provide important implications for healthcare practice and policy, with strengthen diagnosis and treatment of cognitive-based neurological diseases in children.
 CONCLUSION: This research paper concludes with a discussion of the ethical and legal considerations surrounding the use of intelligent computing models in healthcare, as well as future research directions in this area.

Intelligent Voice Mail System

As technology is advancing, people are coming closer to digital life and digital communication. In this modern period, there are many ways to communicate with others through the internet. Common mode of communication among people through internet is Email. A lot of Business and personal information is exchanged over emails in today's time. Some people cannot use these technologies because either they are illiterate or cannot see the screen. Using this system entitled Voice-based Bare Email for Visually Impaired, it will make the email system very easily accessible to visually challenged people and also help society. This application uses text-to-speech and speech-to-text converters so that visually challenged people can operate the system easily. This system reduces the complexity of remembering the characters or information regarding keyboard shortcuts. Every function will be based on simple voice commands so that those people can easily make use of the technology. It also includes various expert systems, natural language processing, machine vision, and speech recognition. To understand and analyze human language such as English by extracting metadata from keywords, emotions, relations, and concepts in Natural Language Processing

Transformers: Statistical interpretation, architectures and applications

Transformers have been widely recognized as powerful tools to analyze multiple tasks due to its state-of art multi-head attention spaces, such as Natural Language Processing (NLP), Computer Vision (CV) and Speech Recognition (SR). Inspired by its abundant designs and strong functions on analyzing input data, we would like to start from the various architectures, further proceed to the investigation on its statistical mechanism and inference and then introduce its applications on dominant tasks. The underlying statistical mechanisms arouse our interests and intrigue us to investigate it in a higher level, and this surveys will focus on its mathematical foundations and then use the principles to try to analyze the reasons for its excellent performance on many recognition scenarios.