Speech Recognition Applications Research Articles

Enhancing the Interaction between Deaf/Hard of Hearing and Hearing People through the Use of Modern Technologies

The rapid advance of computer technologies has unlocked numerous benefits and opportunities that make life easier. This article examines the use of speech recognition applications facilitating the communication between deaf and hearing people. The analysis is based on the interviews with Russian sign language users who rely on various applications, such as Dragon NaturallySpeaking, VoiceNavigator, Roger Speech to Text, and Ava Translator, during the communication process. Although the majority of respondents had low literacy levels due to the challenges often faced by deaf children while studying, they have successfully mastered high-tech gadgets, allowing them to engage more fully in the interaction with oral language speakers. The demand for speech recognition applications stems from the lack of Russian sign language interpreters and the wide range of problematic situations that deaf people encounter in their daily lives. However, the study highlights that many of the existing assistive applications are still not deaf-friendly, thus making deaf people continue to depend on support from sign language interpreters. The results obtained underscore the need to address the limitations of these applications by incorporating feedback from deaf users.

Extracting speech spectrogram of speech signal based on generalized S-transform.

In speech signal processing, time-frequency analysis is commonly employed to extract the spectrogram of speech signals. While many algorithms exist to achieve this with high-quality results, they often lack the flexibility to adjust the resolution of the extracted spectrograms. However, applications such as speech recognition and speech separation frequently require spectrograms of varying resolutions. The flexibility of an algorithm in providing different resolutions is crucial for these applications. This paper introduces the generalized S-transform, and explains its fundamental theory and algorithmic implementation. By adjusting parameters, the proposed method flexibly produces spectrograms with different resolutions, offering a novel and effective approach to obtain speech signal spectrograms. The algorithm enhances the traditional Stockwell transform (S-transform) by incorporating a low-pass filtering function and introducing two adjustable parameters. These parameters modify the Gaussian window function of the basic S-transform, resulting with the generalized S-transform with customizable time-frequency resolution. Finally, this paper presents simulation experiments using both synthesized signals and real speech datas, comparing with the generalized S-transform with several commonly used spectrogram extraction algorithms. The experiments demonstrate that the generalized S-transform is feasible and effective, particularly when it is combined with the generalized fundamental frequency profile. The results indicate that this method is a viable and effective in obtaining spectrograms of speech signals, and has potential application in speech feature extraction and speech recognition. The pure speech dataset used in the experiments is sourced from a downloadable database and partially from a recorded speech set.

Call Center Monitoring for Emergency Services

Speech recognition applications can increase the efficiency of simple tasks and greatly expand the range of tasks that call center operators perform. To increase the productivity of the public security management center, a software module was created which analyzes incoming calls, converts them into text form and places this text in the database. Operators are given the opportunity to read the call text after the end of the call to better understand its content and to improve the quality of the corresponding response. The article discusses the module, which was created to convert the incoming calls into text form. The service is a .NET Core Web API application created using the C# programming language. It uses a NuGet package to communicate with Google’s Speech service. The components of the system are described, the JSON format of the message is given, the structure of the database is discussed, the corresponding software codes of the module are given. The article also shows the customer irritating factors, analyzes the situations that create a series of calls, presents the results of customer surveys, which relate to the needs of customers in terms of waiting time. The paper discusses the mechanism for prioritizing calls, the algorithm for its implementation, as well as ways to manage non-targeted calls. The article shows the pros and cons of a system for queuing priorities.

Recognition and Spectral Analysis of Adi Digits

This article presents a method for automatically recognizing spoken digits in the Adi language using the highly effective open-source voice recognition toolkit, “Kaldi”. The study also includes an analysis of the formant frequencies and spectral characteristics of Adi digits. Adi is a zero-resource indigenous tribal language of Arunachal Pradesh with its roots in Tibeto-Burman. UNESCO's 2009 Atlas of the World's Languages in Danger classifies Adi as an endangered language in India. The research utilizes a modest digit corpus from 42 native Adi speakers, employing Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP) features from continuous Adi speech utterances. The study explores both Monophone and Triphone models for digit recognition, with notable improvements in accuracy from 79.14% in the Monophone model to 84.29%, 88.43%, and 90.86% in the Triphone models (Tri1, Tri2, and Tri3). This proposed model holds promise for the development of Adi language speech recognition applications.

A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges

Accessibility in online platforms is a critical concern in our increasingly digital world, where information and services are predominantly accessed through the Internet. The purpose of this systematic review is to provide a comprehensive overview of the current state of the art in online accessibility technologies, and it is focused on key tools such as sign language recognition, speech-to-text, text-to-speech, and voice recognition. Despite advancements in digital inclusivity, numerous technical limitations persist, which limit the accessibility of online content for individuals with disabilities. Our findings indicate that while speech and voice technologies have achieved good accuracies and low word error rates, further research is needed to improve the accuracy and usability of sign language recognition systems, especially for continuous sign language recognition, as they have low accuracy. In this review, we analyzed research articles and publications from well-known databases, including Google Scholar, Elsevier, IEEE Xplore, and Springer. In order to ensure a high standard of quality, we applied the PRISMA 2020 and PEDro methodologies to quantitatively and qualitatively filter the thousands of articles provided by these databases, and we selected only studies that were related to our study. Key areas of investigation included the performance and accuracy of sign language interfaces, speech-to-text, text-to-speech, and speech recognition applications and the compatibility of these technologies with different platforms and devices. This review also explores the role of emerging technologies such as artificial intelligence (AI) and machine learning (ML) in enhancing accessibility and personalizing user experiences. Through a critical analysis of current solutions and a discussion of existing gaps, this paper offers insights into potential improvements and future directions for creating more accessible online environments. The findings might be valuable to researchers and developers dedicated to promoting digital inclusivity and equality.

Enhancing machine learning recommendation systems with CNN architectures: Applications and innovations in computer vision and speech recognition

Abstract. This paper provides an in-depth analysis of enhancing machine learning recommendation systems using Convolutional Neural Network (CNN) architectures, with a focus on their applications in computer vision and speech recognition. Traditional recommendation systems often struggle with scalability and the complexity of high-dimensional data. By integrating CNNs, these systems can utilize advanced feature extraction and representation learning to more effectively process and analyze diverse data sources. Our study demonstrates significant improvements in recommendation accuracy and personalization through CNN-enhanced systems. We discuss the architecture, design principles, and advantages of CNNs, supported by case studies across various domains. Our findings illustrate the potential of CNNs to revolutionize recommendation systems by addressing existing limitations and offering innovative solutions for real-time, high-quality recommendations. This research emphasizes the importance of advanced machine learning techniques in creating robust and scalable recommendation systems, paving the way for future advancements and applications in multiple fields.

Deep Learning Applications in Natural Language Processing and Optimization Strategies

In recent years, deep learning has made significant advancements in the field of natural language processing (NLP), driving the development of language understanding and generation tasks. Deep learning utilizes multi-layer neural networks to automatically extract and represent complex data features. This paper first introduces the basic principles of deep learning and mainstream frameworks (such as TensorFlow and PyTorch) and discusses core NLP tasks, including word embedding, language modeling, and text generation. Subsequently, it analyzes specific practices of deep learning applications in text classification, machine translation, automatic summarization, dialogue systems, and speech recognition. Further discussion covers model optimization methods, including structural optimization (such as RNN, LSTM, GRU, and Transformer), data preprocessing and feature engineering, hyperparameter tuning, and accelerated computation. Finally, it explores cutting-edge optimization strategies like federated learning, model compression, self-supervised learning, and transfer learning, proposing future research directions and challenges. This paper aims to systematically analyze the applications and optimization strategies of deep learning in NLP.

Development of high sensitivity composite sensors for proprioceptive applications

This study describes the fabrication of extremely sensitive piezoresistive composite sensors designed to detect human motion and speech. Starting out, a solution of thermoplastic polyurethane (TPU) was formed in dimethyl formamide (DMF) with a concentration of 35 % weight to volume. The solution was subsequently spun using a custom-built centrifugal spinning setup to produce TPU fibers. Following their fabrication through spinning, TPU fibers were immersed in a solution of carbon nanoparticles (CNPs) dispersed in tetra hydrofuran (THF), having a concentration of 25 %w/v, for dip coating TPU fibers with CNPs. This resulted in the formation of highly piezoresistive fibers having strain sensing capability. These fibers were then spun into the form of a yarn and tested as a strain sensor for proprioceptive applications. The composite sensors exhibited exceptional repeatability in tests involving continuous stretching and relaxing for more than 5000 cycles. The composite strain sensor demonstrated remarkable extensibility as well. The composite strain sensor was attached to different body parts such as the elbow, knees, fingers, and ankles to detect and track motion. It was found that the sensor could measure and track the angle, position, and frequency of motion in all of these scenarios. The sensor's remarkable sensitivity allowed it to detect different spoken words and letters, in addition to recognizing the action of swallowing in humans. The results show that the newly developed composite strain sensors are suitable for proprioceptive and speech recognition applications in soft robotics, wearable devices, and human-machine interactions.

Multilingual Speech Transcription and Translation System

This project implements a multilingual speech recognition and translation system using Python. The system leverages libraries such as `pygame` for audio playback, `gTTS` for text-to-speech conversion, `speech_recognition` for capturing and recognizing spoken language, and Machine Translation model for translating text between languages. Users interact with the system via a Streamlit-based web interface, where they can select the input and target languages and control the start and stop of the translation process. The program can auto-detect the input language if the user chooses, and it provides real-time feedback by displaying the recognized text, translating it into the chosen target language, and converting the translated text into speech. At the core of the system is a continuous loop that listens for speech input through the microphone, processes the audio to recognize the spoken words, and then translates the recognized text into the desired language. The translated text is not only displayed but also spoken aloud using the text-to-speech functionality. The interface is user-friendly, with clear indications of the current status (listening, processing, recognized text, translated text) and handles errors gracefully, providing feedback in case of issues. This project demonstrates a practical application of speech recognition and translation technologies, offering a seamless and interactive multilingual communication tool

Supervised Learning: An InDepth Analysis

Supervised learning pivotal machine learning paradigm wherein models are trained on labeled datasets. They predict outcomes or classify data. It includes methodologies and diverse applications of supervised learning. Emphasizing significance in modern technology. Key methodologies encompass linear regression logistic regression. Also decision trees, support vector machines neural networks. Each with unique advantages for specific tasks. Versatility is demonstrated through applications in image and speech recognition. Natural language processing, medical diagnosis and financial forecasting also highlighted. Challenges include overfitting data quality, interpretability. Scalability discussed emphasizing areas for ongoing research. Future directions point towards transfer learning. Integration with semisupervised and unsupervised methods. Explainable AI and quantum machine learning promise further enhancements. They will impact supervised learning across various domains. Key words: Supervised learning, Machine learning, Labeled datasets, Algorithm training, Outcome prediction, Data classification, Input-output pairs, Model generalization, Accurate predictions, Real-world applications, Spam filters, Recommendation systems, Medical diagnosis, Autonomous driving, Efficiency enhancement, Decision-making, User experience, Data handling.

Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation

Comprehensive clinical documentation is crucial for effective healthcare delivery, yet it poses a significant burden on healthcare professionals, leading to burnout, increased medical errors, and compromised patient safety. This paper explores the potential of generative AI (Artificial Intelligence) to streamline the clinical documentation process, specifically focusing on generating SOAP (Subjective, Objective, Assessment, Plan) and BIRP (Behavior, Intervention, Response, Plan) notes. We present a case study demonstrating the application of natural language processing (NLP) and automatic speech recognition (ASR) technologies to transcribe patient-clinician interactions, coupled with advanced prompting techniques to generate draft clinical notes using large language models (LLMs). The study highlights the benefits of this approach, including time savings, improved documentation quality, and enhanced patient-centered care. Additionally, we discuss ethical considerations, such as maintaining patient confidentiality and addressing model biases, underscoring the need for responsible deployment of generative AI in healthcare settings. The findings suggest that generative AI has the potential to revolutionize clinical documentation practices, alleviating administrative burdens and enabling healthcare professionals to focus more on direct patient care.

Speech Emotion Recognition

Speech emotion recognition is a vital area of research with applications starting from human-computer interaction to mental health monitoring. This paper will provide a comprehensive survey of the techniques, methods, applications, and challenges in speech emotion recognition. It begins by checking the significance of recognizing emotions from speech and its diverse applications across various fields. The paper then shows the method employed for emotion speech recognition, encompassing traditional machine learning techniques, such as support vector machines and Gaussian mixture models, as well as contemporary approaches, including deep learning and multimodal fusion. Moreover, it examines benchmark datasets commonly used for training and evaluation purposes in emotion speech recognition research. Speech Emotion Recognition (SER) has a wide range of applications and there has been a lot of research going on in this fascinating area in recent years. However, the entertainment sector suffers from a lack of study in this research. Many of use Neural Network (NN) and Long Short-Term Memory (LSTM) architectures to categorize the emotions in audio recordings captured by actors expressing various emotions. Moreover, our survey explores real-world applications of emotion speech recognition like virtual assistants, health sector, market sector, education, Mental health diagnosis. The paper discusses the challenge associated with emotion speech recognition, including the variability of emotional expressions, cultural influences, humor and privacy concerns. In future it will help others to deal with many Noisy dataset and various cultural effects of emotion.

Wake Word and Speech Recognition Application on Edge Device: A Case of Improving the Electric Wheelchair

Wake Word and Speech Recognition Application on Edge Device: A Case of Improving the Electric Wheelchair

A Novel Approach for Realizing Loop-Filter in Low-Distortion Noise-Coupled ΣΔ Modulators

This paper presents an innovative general structure for a single-loop Sigma-Delta ([Formula: see text]) modulator that combines low-distortion and noise-coupled techniques to achieve high-resolution for low-power applications. The low-distortion technique adjusts the signal transfer function of the proposed structure to unity, while the noise-coupled technique enhances the order of quantization noise-shaping at the modulator output. These techniques were incorporated into the design to increase the modulator order without requiring additional operational amplifiers during its circuit implementation, resulting in a low-power modulator compared to similar structures. To reduce the number of required amplifiers, a second-order infinite impulse response (IIR) filter was used in the modulator loop filter, in place of two integrators. To assess the proposed structure’s performance, a third-order modulator sample was implemented and simulated for speech recognition applications, specifically, digital hearing aids, using 180[Formula: see text]nm complementary metal oxide semiconductor (CMOS) technology. The results indicate that for a third structure with a sampling rate of 64, an input sine signal of −6[Formula: see text]dBFS and a sampling frequency of 2.56[Formula: see text]MHz, the signal to noise plus distortion (SNDR) was 81.9[Formula: see text]dB, and the dynamic range (DR) was 88[Formula: see text]dB for a 20[Formula: see text]KHz bandwidth. The modulator’s power consumption was 126.9[Formula: see text][Formula: see text]W. Circuit and system-level simulations demonstrate the effectiveness of the proposed method.

Highly-performance nonwoven pressure sensors with enhanced breathability and durability for human health monitoring

In recent years, the rapid development of smart wearable devices has resulted in significant advances in flexible pressure sensors, which have found application in speech recognition, health monitoring, as well as motion tracking. There is, however, still a significant challenge associated with the low-cost preparation of flexible pressure sensors with high sensitivity and permeability. An innovative nonwoven-based flexible pressure sensor is presented in this paper consisting of a conductive sensing layer composed of reduced graphene oxide-treated polyester hydrophilic nonwoven, an electrode layer comprised of carbon nanotube-printed polyester plain nonwoven electrodes, and a top pressure layer composed of hydroentangled viscose dome structure nonwoven. The pressure sensor exhibits high sensitivity (5.43% kPa−1 in the range of 0–10.12 kPa), excellent permeability (807.2 mm s−1), rapid response and recovery time (73/73 ms), a wide sensing range (0–185 kPa), and remarkable cycling durability (∼20,000 cycles). The sensor exhibits superhydrophobic properties, as indicated by a water contact angle of up to 155°. Based on its commendable performance, the sensor demonstrates the capability to discern various pressure signals, enabling the potential for encrypted transmission of military information. Additionally, it proves valuable in applications such as health monitoring and motion tracking. Consequently, the flexible pressure sensor has significant potential for application in areas such as data encryption, health monitoring, and motion detection.

SPEECH RECOGNITION SYSTEM

Speech recognition technology has witnessed remarkable progress in recent years, fueled by advancements in machine learning, deep neural networks, and signal processing techniques. This paper presents a comprehensive review of the current state-of-the-art in speech recognition systems, highlighting key methodologies and breakthroughs that have contributed to their improved performance. The paper explores various aspects, including acoustic modeling, language modeling, and the integration of contextual information, shedding light on the challenges faced and innovative solutions proposed in the field. Furthermore, the paper discusses the impact of large-scale datasets and transfer learning on the robustness and adaptability of speech recognition models. It delves into recent developments in end-to-end models and their potential to simplify the architecture while enhancing accuracy. The integration of real-time and edge computing for speech recognition applications is also explored, emphasizing the implications for practical implementations in diverse domains such as healthcare, telecommunications, and smart devices. In addition to reviewing the current landscape, the paper provides insights into future prospects and emerging trends in speech recognition research. The role of multimodal approaches, incorporating visual and contextual cues, is discussed as a potential avenue for further improvement. Ethical considerations related to privacy and bias in speech recognition systems are also addressed, emphasizing the importance of responsible development and deployment. By synthesizing current research findings and anticipating future directions, this paper contributes to the evolving discourse on speech recognition technologies, providing a valuable resource for researchers, practitioners, and industry professionals in the field. Key Words: Real-time processing , Machine learning , Deep neural networks , Technology advancements , Contextual information , Large-scale datasets Transfer learning , End-to-end models , Real-time processing Edge computing , Multimodal approaches Ethical considerations , Privacy , Bias , Future prospects Research review.

Utilizing Deep Learning Techniques for the Classification of Spoken Languages in India

In Western countries, speech-recognition applications are accepted. In East Asia, it isn't as common. The complexity of the language might be one of the main reasons for this latency. Furthermore, multilingual nations such as India must be considered in order to achieve language recognition (words and phrases) utilizing speech signals. In the last decade, experts have been clamoring for more study on speech. In the initial part of the pre-processing step, a pitch and audio feature extraction technique were used, followed by a deep learning classification method, to properly identify the spoken language. Various feature extraction approaches will be discussed in this review, along with their advantages and disadvantages. Also discussed were the distinctions between various machine learning and deep learning approaches. Finally, it will point the way for future study in Indian spoken language recognition, as well as AI technology.

A Spiking LSTM Accelerator for Automatic Speech Recognition Application Based on FPGA

Long Short-Term Memory (LSTM) finds extensive application in sequential learning tasks, notably in speech recognition. However, existing accelerators tailored for traditional LSTM networks grapple with high power consumption, primarily due to the intensive matrix–vector multiplication operations inherent to LSTM networks. In contrast, the spiking LSTM network has been designed to avoid these multiplication operations by replacing multiplication and nonlinear functions with addition and comparison. In this paper, we present an FPGA-based accelerator specifically designed for spiking LSTM networks. Firstly, we employ a low-cost circuit in the LSTM gate to significantly reduce power consumption and hardware cost. Secondly, we propose a serial–parallel processing architecture along with hardware implementation to reduce inference latency. Thirdly, we quantize and efficiently deploy the synapses of the spiking LSTM network. The power consumption of the accelerator implemented on Artix-7 and Zynq-7000 is only about 1.1 W and 0.84 W, respectively, when performing the inference for speech recognition with the Free Spoken Digit Dataset (FSDD). Additionally, the energy consumed per inference is remarkably efficient, with values of 87 µJ and 66 µJ, respectively. In comparison with dedicated accelerators designed for traditional LSTM networks, our spiking LSTM accelerator achieves a remarkable reduction in power consumption, amounting to orders of magnitude.

Acoustic and Spectral Analysis of Adi Triphthongs

Adi, a zero-resource indigenous tribal language of Arunachal Pradesh, originated in Tibeto-Burman. According to the “UNESCO Atlas of the World's Languages in Danger of 2017” report, Adi is critically endangered. In this research, two new Adi triphthongs, /uai/ [uai] and /oai/ [ɔai], are proposed in the phoneme set of the Adi language. Adi has 51 phonemes, including 14 monophthongs (7 short and 7 long vowels), 19 diphthongs, two triphthongs, and 16 consonants. This research analysed Adi triphthongs’ spectral and acoustic properties, including intensity variation, long-term average spectrum (LTAS), and formant distribution. The mean of the first four formant frequencies of male and female Adi speakers is measured, which may be the identification of these two phones in the Adi speech recognition systems. Four different Adi words for each triphthong are recorded from 68 individual native Adi speakers (32 males and 36 females) in Arunachal Pradesh. This study may be helpful for phonetic modelling to build speech recognition applications for the Adi language.

Chinese dialect speech recognition: a comprehensive survey

As a multi-ethnic country with a large population, China is endowed with diverse dialects, which brings considerable challenges to speech recognition work. In fact, due to geographical location, population migration, and other factors, the research progress and practical application of Chinese dialect speech recognition are currently at different stages. Therefore, exploring the significant regional heterogeneities in specific recognition approaches and effects, dialect corpus, and other resources is of vital importance for Chinese speech recognition work. Based on this, we first start with the regional classification of dialects and analyze the pivotal acoustic characteristics of dialects, including specific vowels and tones patterns. Secondly, we comprehensively summarize the existing dialect phonetic corpus in China, which is of some assistance in exploring the general construction methods of dialect phonetic corpus. Moreover, we expound on the general process of dialect recognition. Several critical dialect recognition approaches are summarized and introduced in detail, especially the hybrid method of Artificial Neural Network (ANN) combined with the Hidden Markov Model(HMM), as well as the End-to-End (E2E). Thirdly, through the in-depth comparison of their principles, merits, disadvantages, and recognition performance for different dialects, the development trends and challenges in dialect recognition in the future are pointed out. Finally, some application examples of dialect speech recognition are collected and discussed.