Intelligent Speech Recognition Research Articles

Intelligent Speech Recognition using Fractal Amended Grasshopper Optimization Algorithm with Deep Learning Approach

Intelligent Speech Recognition using Fractal Amended Grasshopper Optimization Algorithm with Deep Learning Approach

Smart Voice Based Medical Chatbot for Disease Prediction

The global shortage of medical resources has intensified in recent years, exacerbated by a growing and aging population, particularly following the pandemic. However, advancements in robotics and artificial intelligence have empowered healthcare professionals to address these challenges. Intelligent Speech Technology (IST) is one such innovation that has helped doctors and patients enhance medical efficiency and reduce cumbersome tasks. Despite this, complex hospital environments with background noise and variations in patient pronunciation have limited IST's broader application. Yet, as machine learning technologies rapidly advance intelligent speech recognition capabilities, these obstacles are expected to be overcome in the near future. This project focuses on the concept of an AI-based voice- assisted chatbot for medical disease prediction. Deployed on a Raspberry Pi as a standalone hardware device, it utilizes natural language processing (NLP) and machine learning (ML) techniques to detect diseases by interacting with users through a series of questions. A groundbreaking innovation, the Speech-Assisted Chatbot for Medical Infectious Disease Prediction, promises to transform healthcare delivery. By leveraging AI-driven algorithms and natural language processing, it facilitates quick diagnosis and prescription generation through spoken interactions with patients. This simplifies and accelerates procedures, reduces error risks, improves patient care standards, saves valuable time for healthcare professionals, and enhances service availability, all contributing to better outcomes and more efficient healthcare processes.. Key Words: chatbot, NLP, Machine learning, Medical diagnosis, Speech, Artificial Intelligence etc.

Association between tooth loss-related speech and psychosocial impairment with cognitive function: A pilot study in Hong Kong's older population.

Tooth loss has been associated with cognitive decline, but the underlying mechanisms involving speech and psychosocial impairment remain unclear. To investigate the impact of tooth loss-related speech and psychosocial impairment on cognitive function in Hong Kong's older population. Seventy-six Cantonese-speaking participants between the ages of 51-92 were classified into three groups: patients with complete dentures (CD), partially edentulous patients with less than 10 occluding tooth pairs (OU <10), and at least 10 occluding tooth pairs (OU ≥10). Cognitive function was assessed using the Montreal Cognitive Assessment Hong Kong Version, One-minute Verbal Fluency Task and Hayling Sentence Completion Test. Objective and subjective speech assessments were carried out using artificial intelligence speech recognition algorithm and a self-designed speech questionnaire. The impact of tooth loss on psychosocial condition was evaluated by the Reading the Mind in the Eyes Test and a self-designed questionnaire. Statistical analyses (one-way ANOVA, ANCOVA, Kruskal-Wallis test, Spearman correlation test) were performed. Tooth loss was significantly associated with lower cognitive function (p = .008), speech accuracy (p = .018) and verbal fluency (p = .001). Correlations were found between cognitive function and speech accuracy (p < .0001). No significant difference in tooth loss-related psychosocial impact was found between the three groups. While warranting larger sample sizes, this pilot study highlights the need for further research on the role of speech in the association between tooth loss and cognitive function. The potential cognitive impact of tooth retention, together with its known biological and proprioceptive benefits, supports the preservation of the natural dentition.

Application of speech recognition algorithm based on interactive artificial intelligence system in English video teaching system

Social robots are intelligent programs that can autonomously run, automatically publish information, and interact on social media platforms. This article focuses on the speech recognition algorithm of interactive artificial intelligence systems, combined with the Hanning window function and the Vitit algorithm, and explores its application value in English video teaching systems through detailed experiments. Adopting speech feature extraction technology provides strong support for the subsequent recognition process. The application of Hanning window function has a positive impact on speech signal preprocessing. The most indispensable algorithm in speech recognition models is the Vitit algorithm, which successfully solves the problem of time variation and continuity of speech signals. This study successfully applied speech recognition algorithms based on interactive artificial intelligence systems to English video teaching systems. By combining speech feature extraction, Hanning window function, and Vitit algorithm, a system with strong recognition ability and interactivity was constructed. The English video teaching system aims to improve students’ learning process. By combining interactive artificial intelligence speech recognition technology with English video teaching, it can improve students’ experience in learning English and also meet their personalized learning habits.

Improving elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions

Generative artificial intelligence (GAI) and automatic speech recognition (ASR) have ushered in promising tools for foreign language learning, notably GAI chatbots. This study investigated the impact of GAI chatbots on elementary school English as a foreign language (EFL) learners' speaking skills, focusing on two interaction configurations—individual and paired. Eighty-five elementary school EFL learners participated in a three-week summer program, engaging in daily 45-min interactions with CoolE Bot. The participants were randomly assigned to three groups: (1) individual interaction with CoolE Bot (I-Bot group), (2) paired interaction with CoolE Bot (P-Bot group), and (3) interaction with teachers and peers in a conventional English classroom (No-Bot group). In each class, participants in the Bot group received worksheets with a topic, prompts, and vocabulary to guide their interactions with CoolE Bot, while those in the No-Bot group also received worksheets for comparable activities. Quantitative (English-speaking tests) and qualitative data (semi-structured interviews) were collected and analyzed. Results revealed that the I-Bot and P-Bot groups' post-test speaking skills were significantly higher than those of the No-Bot group. CoolE Bot significantly improved the speaking skills of EFL learners. Both individual and paired interactions with CoolE Bot demonstrated positive effects, with no significant differences between groups. Interviews highlight CoolE Bot's adeptness in coherent interaction, charismatic conversational style with a human-like voice, diverse topic discussions tailored to learners' interests, and supportive functions. The participants found GAI chatbot-assisted EFL speaking enjoyable, motivating, and engaging appreciating its cartoonish, human-like characters, conversational style, and voice. Additionally, CoolE Bot fostered rapport and a supportive environment enhancing learners' confidence and reducing anxiety regarding EFL speaking. Individual interactions encourage personalized engagement and self-directed learning, whereas paired interactions involve social dynamics, shared learning experiences, and mutual resolution of language challenges.

Exploring the effects of intelligent personal assistants on bilingual education teachers’ L2 speaking proficiency

ABSTRACT Communication in a second and foreign language (L2) is regarded as the most challenging and anxiety-inducing language skill for L2 learners and teachers. The emergence of artificial intelligence and automatic speech recognition (ASR) has opened new avenues for enhancing L2 interaction, with intelligent personal assistants (IPAs) showing significant potential. While prior research has predominantly focused on learners, using IPAs for bilingual education (BE) teachers has received relatively little attention. The study investigated the effects of IPAs (i.e. Google Assistant) on BE teachers’ L2 speaking proficiency and their perceptions of IPA-assisted L2 speaking. Thirty Taiwanese BE teachers who were L2 English speakers participated in a semester-long IPA-mediated interaction program. These teachers interacted with Google Assistant in their daily lives. Both quantitative (English speaking proficiency tests) and qualitative data (semi-structured interviews) were collected and analyzed. The results revealed that Google Assistant significantly promoted BE teachers’ English-speaking proficiency. Most teachers confirmed that Google Assistant could establish an authentic environment that facilitates communication, increases English-speaking opportunities, enhances practice frequency, and provides scaffolding and immediate feedback, which contribute to their English speaking proficiency. Integrating Google Assistant into lesson preparation proved valuable, enabling teachers to access curriculum-related information efficiently and increasing their motivation to engage with IPAs.

Artificial Intelligence-based Legal Application for Resolving Issues Related to Live-In Relationship

INTRODUCTION: The societal landscape in India has witnessed a very transformative shift in the perspectives on relationships, with an increasing prevalence of live-in couples challenging the traditional norms of marriage. However, this ongoing trend has brought about a huge surge in legal complexities, including recognition, partner rights, property disputes, and inheritance issues. This study proposed an innovative approach that leveraged the potential of Artificial Intelligence and Automatic speech recognition for the registration and redressal of live-in relationship matters.&#x0D; OBJECTIVES: This research explores and seeks for the optimization of the resolution of live-in relationship disputes which occurs in the legal perspective with the help of an AI-based platform. The primary goal of this research was to overcome the physical barriers while ensuring the correct accessibility to legal procedures for the registration and addressing of the grievances related to live-in relationships. &#x0D; METHODS: Here, the methodology followed, starting from the thorough review which was conducted using different resources from Scopus, PubMed, and ResearchGate. This research explored the increasing complaints and varying victim counts in live-in relationship cases. This finally attributed to these issues to a lack of physical access to legal remedies.&#x0D; RESULTS: This study also emphasized the major significance of AI-driven redressal processes in the real-time alleviation of the hurdles and challenges associated with live-in relationship cases. The proposed framework and platform aimed to offer an alternative means for the individuals who were unable to physically approach the authorities, facilitating a more efficient and seamless way of legal resolution more quickly.&#x0D; CONCLUSION: This study advocates for the integration of AI and AST technologies in the legal domain, specifically for addressing live-in relationship issues. The implementation of such a system had the potential to bridge gaps in its accessibility, thereby contributing to a more inclusive and efficient legal framework for individuals who are passionately involved in live-in relationships.

Laser-Induced Skin-like Flexible Pressure Sensor for Artificial Intelligence Speech Recognition.

Skin-like flexible pressure sensors with good sensing performance have great application potential, but their development is limited owing to the need for multistep, high-cost, and low-efficiency preparation processes. Herein, a simple, low-cost, and efficient laser-induced forming process is proposed for the first time to prepare a skin-like flexible piezoresistive sensor. In the laser-induced forming process, based on the photothermal effect of graphene and the foaming effect of glucose, a skin-like polydimethylsiloxanes (PDMS) film with porous structures and surface protrusions is obtained by using infrared laser irradiation of the glucose/graphene/PDMS prepolymer film. Further, based on the skin-like PDMS film with a graphene conductive layer, a new skin-like flexible piezoresistive sensor is obtained. Due to the stress concentration caused by the surface protrusions and the low stiffness caused by the porous structures, the flexible piezoresistive sensor realizes an ultrahigh sensitivity of 1348 kPa-1 at 0-2 kPa, a wide range of 200 kPa, a fast response/recovery time of 52 ms/35 ms, and good stability over 5000 cycles. The application of the sensor to the detection of human pulses and robot clamping force indicates its potential for health monitoring and soft robots. Furthermore, in combination with the neural network (CNN) algorithm in artificial intelligence technology, the sensor achieves 95% accuracy in speech recognition, which demonstrates its great potential for intelligent wearable electronics. Especially, the laser-induced forming process is expected to facilitate the efficient, large-scale preparation of flexible devices with multilevel structures.

Transcribing in the digital age: qualitative research practice utilizing intelligent speech recognition technology.

The digital revolution provides many opportunities for researchers to develop and evolve data collection methods. A key process in qualitative research data collection is the transcription of interviews, focus groups or fieldwork notes. Transcription is the process of converting audio, video or notes into accessible written format for qualitative data analysis. Transcribing can be time intensive, costly and laborious with decisions and methods of transcribing requiring transparency. The development of intelligent speech recognition technology can change how qualitative data is transcribed. This methods paper describes audio data transcribing, current challenges, opportunities and implications in using intelligent speech recognition technology for transcribing. An application of this methodology is presented.

Research on the Innovation of University English Teaching Mode Driven by Artificial Intelligence

Abstract With the continuous deepening and development of information technology in the field of education, the combination of artificial intelligence technology and university English teaching has become a trend. This paper constructs a human-machine symbiotic English teaching model based on intelligent speech recognition, which takes into account university English learning abilities and learning objectives. Specifically, the multi-scale attention mechanism is used instead of the Conformer model to extract the global information of speech features and the local information of different scales and fuse them to learn more speech representations of different scales so as to better recognize the teacher and student behaviors in the English classroom. Meanwhile, combining the teaching mechanism of human-computer symbiosis, we construct a human-computer symbiosis English teaching model for speech recognition in an intelligent classroom and receive feedback after teaching experiments. The occupancy rate of teacher and student behaviors in the English classroom is 50.86% and 49.14%, respectively, and the conversion rate of teacher and student behaviors is 29.78%, which achieves a high level of teacher-student interaction in this model. In addition, the class utilizing the English teaching model of this paper was 5.02 points higher than the traditional class after one semester of teaching, P=0.038&lt;0.05, and the results showed a significant difference. Through the comparison of cases and teaching experiments, the reasonableness of the English teaching model based on speech recognition is demonstrated, and the research content and problem areas of this paper are summarized.

The Application of Intelligent Speech Recognition in the Teaching of Spoken English in Colleges and Universities

Abstract With the rapid development of science and technology, the use of artificial intelligence in the field of higher education is becoming more and more common. The use of intelligent speech recognition technology in English teaching in colleges and universities is on the rise. The research utilizes speech recognition logic to create a hybrid CTC/Attention model for teaching spoken English in colleges and universities. Then we compare and analyze the performance of artificial speech recognition models, such as the HMM model and CTC/Attention model, and explore the effects of teaching English speaking in colleges and universities using this model. In terms of the overall recognition rate, with the increase of the test vocabulary, the recognition rates of both the artificial speech recognition model and the HMM model decrease, while the CTC/Attention model shows an increasing trend. Except for intonation, the CTC/Attention model has the best recognition rate performance across all dimensions. Through the college-speaking teaching experiment based on the CTC/Attention model, the students’ total English speaking performance, accuracy, fluency, and complexity were improved. The Sig values of total speaking achievement, accuracy, fluency, and complexity are less than 0.05. College English-speaking teaching with intelligent speech recognition has a significant positive impact on the improvement of student’s English-speaking achievement, as indicated.

An Intelligent Speech Recognition Method Based on Stable Learning

An Intelligent Speech Recognition Method Based on Stable Learning

Intelligent speech recognition algorithm in multimedia visual interaction via BiLSTM and attention mechanism

With the rapid development of information technology in modern society, the application of multimedia integration platform is more and more extensive. Speech recognition has become an important subject in the process of multimedia visual interaction. The accuracy of speech recognition is dependent on a number of elements, two of which are the acoustic characteristics of speech and the speech recognition model. Speech data is complex and changeable. Most methods only extract a single type of feature of the signal to represent the speech signal. This single feature cannot express the hidden information. And, the excellent speech recognition model can also better learn the characteristic speech information to improve performance. This work proposes a new method for speech recognition in multimedia visual interaction. First of all, this work considers the problem that a single feature cannot fully represent complex speech information. This paper proposes three kinds of feature fusion structures to extract speech information from different angles. This extracts three different fusion features based on the low-level features and higher-level sparse representation. Secondly, this work relies on the strong learning ability of neural network and the weight distribution mechanism of attention model. In this paper, the fusion feature is combined with the bidirectional long and short memory network with attention. The extracted fusion features contain more speech information with strong discrimination. When the weight increases, it can further improve the influence of features on the predicted value and improve the performance. Finally, this paper has carried out systematic experiments on the proposed method, and the results verify the feasibility.

Application and development prospect of AI speech recognition technology

In our daily life, artificial intelligence technology has penetrated all aspects, for us, language communication is a kind of media, but also an important way for us to convey ideas and give orders, so now, language communication has become an important way for us to interact with artificial intelligence. This paper through social research and the use of voice command to the existing speech recognition technology products test method puts forward the current development problems and solutions, the current situation of artificial intelligence speech recognition technology and the future in the field of communication systems and the intelligent home application made a simple introduction.

The Future of Work: Augmentation or Stunting?

The last decade has seen significant improvements in artificial intelligence (AI) technologies, including robotics, machine vision, speech recognition, and text generation. Increasing automation will undoubtedly affect the future of work, and discussions on how the development of AI in the workplace will impact labor markets often include two scenarios: (1) labor replacement and (2) labor enabling. The former involves replacing workers with machines, while the latter assumes that human–machine cooperation can significantly improve worker productivity. In this context, it is often argued that (1) could lead to mass unemployment and that (2) therefore would be more desirable. We argue, however, that the labor-enabling scenario conflates two distinct possibilities. On the one hand, technology can increase productivity while also promoting “the goods of work,” such as the opportunity to pursue excellence, experience a sense of community, and contribute to society (human augmentation). On the other hand, higher productivity can also be achieved in a way that reduces opportunities for the “goods of work” and/or increases “the bads of work,” such as injury, reduced physical and mental health, reduction of autonomy, privacy, and human dignity (human stunting). We outline the differences of these outcomes and discuss the implications for the labor market in the context of contemporaneous discussions on the value of work and human wellbeing.

Speech Signal Processing of Industrial Speech Recognition

The noise in the industrial production environment is more complex than the application scenario in the home or office environment, and the most of the noise loudness is much larger. Automatic speech recognition(ASR) system will fail when the front-end speech signal processing SNR is too poor. At this time the front-end speech signal processing ability is particularly important. The product solution mentioned in this article involves multiple point. It includes omnidirectional Microphones Array scheme selection, selection of Mic monomer, sampling, digitization, noise reduction and echo processing, so as to improve the overall SNR and improve the speech recognition rate under noise,therefore, it has become an AR head-mounted computer for industrial intelligent speech recognition accepted by many business worldwide.

Stretchable, ultralow detection limit and anti-interference hydrogel strain sensor for intelligent throat speech recognition using Resnet50 neural network

Flexible strain sensors have important applications in wearable electronics, human motion monitoring, and healthcare.

Emotional state analysis based on speech recognition signals

At present, as intelligent speech recognition is widely used, the importance of speech emotion recognition technology is also gradually gaining recognition. Considering the current social pressure, the generation of negative emotions has become particularly common. Therefore, it is necessary to increase the recognition of different emotions by using the speech recognition system, which has been perfected, so as to play a role in anticipation and prevention. In this paper, we focus on the negative effects of negative emotions on people's lives, and use K-nearest classifier to identify different emotions, and accurately identify them, and prevent and monitor them. Finally, the feasibility of this method is confirmed by simulating the process.

Imaginary Speech Recognition Using a Convolutional Network with Long-Short Memory

In recent years, a lot of researchers’ attentions were concentrating on imaginary speech understanding, decoding, and even recognition. Speech is a complex mechanism, which involves multiple brain areas in the process of production, planning, and precise control of a large number of muscles and articulation involved in the actual utterance. This paper proposes an intelligent imaginary speech recognition system of eleven different utterances, seven phonemes, and four words from the Kara One database. We showed, during our research, that the feature space of the cross-covariance in frequency domain offers a better perspective of the imaginary speech by computing LDA for 2D representation of the feature space, in comparison to cross-covariance in the time domain and the raw signals without any processing. In the classification stage, we used a CNNLSTM neural network and obtained a performance of 43% accuracy for all eleven different utterances. The developed system was meant to be a subject’s shared system. We also showed that, using the channels corresponding to the anatomical structures of the brain involved in speech production, i.e., Broca area, primary motor cortex, and secondary motor cortex, 93% of information is preserved, obtaining 40% accuracy by using 29 electrodes out of the initial 62.

Detecting Spoofed Speeches via Segment-Based Word CQCC and Average ZCR for Embedded Systems

Intelligent speech recognition is increasingly used in embedded systems, which is also seriously threatened by malicious speech spoofing attacks. Different from the conventional methods, this article proposes a segment-based anti-spoofing detection (SASD) method for the quick detection of spoofed speeches against embedded speech recognition, which focuses on the anti-spoofing features rather than the contexts of speeches and the voiceprints of speakers. The speeches are divided into word segments and silent segments. Based on constant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> cepstral coefficients (CQCCs), a word CQCC (WCQCC) extraction is first designed for the word segments of speeches. Then, based on short-term zero crossing rate (ZCR), an average ZCR (AZCR) extraction is devised for the silent segments. Combining the WCQCC of word segments and AZCR of silent segments, a biased decision strategy is proposed to quickly determine whether a speech is spoofed. Based on ASVspoof 2021 datasets, extensive experiments are conducted to evaluate the effectiveness of the proposed method. Specifically, our SASD can improve the accuracy of anti-spoofing detection by up to 33.47% and save up to 69.10% of time overhead on embedded devices compared with the existing methods.