Application Of Speech Recognition Technology Research Articles

Deep learning approach for automatic speech recognition in the presence of noise

Accurate recognition of speech in noisy environment is still an obstacle for wider application of speech recognition technology. The robustness of a speech recognition system is heavily influenced by the ability to handle the presence of background noise. In this research work, we propose a model based on Deep Fourier Neural Network (DFNN) for Automatic Speech Recognition (ASR) using LibriSpeech dataset. Most of the existing speech recognition techniques lack the robustness of handling background noise, as a result these techniques are not applicable in real-time. In order to mitigate the challenges of background noise, this research work proposes an efficient recognition technique which analyses in detail the raw audio waveforms using the Deep Fourier Neural Network (DFNN). This novel deep learning approach has a concise architecture and is an efficient approach for automatic speech recognition. The proposed deep learning approach embeds the Fourier transform, which is one of the most popular feature representations transform for audio signal processing. The Fourier transform extracts the core information from waveforms in the form of short term spectra of the speech signal as a function of time. The extracted short term spectra are analyzed deeply in the proposed DFNN model for accurate speech recognition in the presence of noise.

Application of Speech Recognition Technology Based on Multimodal Information in Human-Computer Interaction

Application of Speech Recognition Technology Based on Multimodal Information in Human-Computer Interaction

Application of speech recognition technology based on machine learning for network oral English teaching system

Application of speech recognition technology based on machine learning for network oral English teaching system

Speech recognition technology in radiology

Speech recognition devices are promising tools for the healthcare system. Speech recognition technology has had a relatively long history of use in Western healthcare systems since the 1970s. However, it became widely used at the beginning of the 21st century, replacing medical transcriptionists. This technology is relatively new in home healthcare. Its active development began only in the early 2010s, and its implementation in healthcare started in late 2010. This delay is due to the idiosyncrasies of the Russian language and the limited computational power present at the beginning of the 21st century.
 Currently, complexes of devices and software for speech recognition are used in the voice filling of medical records and can reduce the time for preparing reports for radiological examinations compared with traditional (keyboard) text input.
 The literature review provides a brief history of speech recognition technology development and application in radiography. Key scientific studies showing its efficacy in Western healthcare systems are reflected. Voice recognition technology in the home is demonstrated, and its effectiveness is evaluated. The prospects for further development of this technology in Russian healthcare are described.

Design of Political Online Teaching Based on Artificial Speech Recognition and Deep Learning.

With the emergence of the information age, computers have entered the homes of ordinary people and have become essential daily appliances for people. The integration of people and computers has become more popular and in-depth. Based on this situation, how to make computers and humans communicate intelligently and make human-computer interaction more convenient and practical is the main issue of scientific research and discussion on computers. Language recognition is widely used in industry, the economy, commerce, tourism, style, and sports. The BP neural network based on deep learning has supercomputing functions and is very suitable for speech recognition, which promotes the promotion and application of speech recognition technology in many fields. Different types of applications and diverse scenarios enable users to experience different personal experiences. This paper examines the use of application software for ideological and political theory courses in colleges and universities and the application of application software for ideological and political theory courses in colleges and universities in the management of ideological and political theory courses. Analyzed the use of apps in the management of ideological and political theory courses in colleges and universities, analyzed the possibilities and challenges that this brought to the management of ideological and political theory courses in colleges and universities based on the app, and put forward the necessary countermeasures. Including the development and design of ideological and political theory applications, combining deep learning and CTC algorithms to build acoustic models, using server-client interaction, designing an ideological and political theory course app based on speech recognition and deep learning, and forming an offline speech recognition system software platform, the app provides a certain reference for improving the skills of teaching managers.

Review of research on applications of speech recognition technology to assist language learning

AbstractSpeech recognition technology (SRT) is now widely used in education because of its potential to aid learning, particularly language learning. Nevertheless, SRT has received only limited attention in earlier review studies. The present research aimed to address this gap in the field. To this end, 26 articles published in SSCI journals between 2014 and 2020 were selected and reviewed with respect to domain and skills, technology and their application, participants and duration, measures, reported results, and advantages and disadvantages of SRT. The results showed that English received much more attention than any other language, and scholars mostly focused on facilitating pronunciation skills. Dragon Naturally Speaking and Google speech recognition were the most popular technologies, and their most frequent application was providing feedback. According to the results, college students were involved in research more than any other group, most studies were carried out for less than one month, and most scholars administered a questionnaire or pre-/posttest to collect the data. Positive results related to gains in proficiency and student perceptions of SRT were identified. The study revealed that improved affective factors and enhanced language skills were advantages, whereas a low accuracy rate and insufficiency (i.e. lack of some useful features to support learning efficiently) of SRT were disadvantages. Based on the results, the study puts forward several implications and suggestions for educators and researchers in the field.

Application of Speech Recognition Technology in Chinese English Simultaneous Interpretation of Law

Speech recognition is an important research field in natural language processing. In Chinese and English, which have rich data resources, the performance of end-to-end speech recognition model is close to that of Hidden Markov Model—Deep Neural Network (HMM-DNN) model. However, for the low resource speech recognition task of Chinese English hybrid, the end-to-end speech recognition system does not achieve good performance. In the case of limited mixed data between Chinese and English, the modeling method of end-to-end speech recognition is studied. This paper focuses on two end-to-end speech recognition models: connection timing distribution and attention based codec network. In order to improve the performance of Chinese English hybrid speech recognition, this paper studies how to improve the performance of the coder based on connection timing distribution model and attention mechanism, and tries to combine the two models to improve the performance of Chinese English hybrid speech recognition. In low resource Chinese English mixed data, the advantages of different models are used to improve the performance of end-to-end models, so as to improve the recognition accuracy of speech recognition technology in legal Chinese English simultaneous interpretation.

The Application of Intelligent Speech Recognition Technology in the Tone Correction of College Piano Teaching

In recent years, with the continuous deepening of college teaching reform and the rapid development of artificial intelligence, the teaching methods used in college classrooms have been greatly different from traditional ones. The original teaching programs and teaching methods need to be changed with the development of the times. The main purpose of this article is to improve the correction rate of intelligent voice recognition technology for college piano timbre without violating the safety of artificial intelligence. This article mainly conducts experiments by consulting relevant domestic and foreign literature, using observation and comparison methods, and using some neural network algorithms (BP neural network algorithm and convolutional neural network algorithm) to obtain experimental results, and finally complete our experimental goal-research intelligence The application of speech recognition technology in the tone correction of piano teaching in colleges. The test results show that the use of multiple algorithms can improve the application of intelligent speech recognition technology in the tone correction of piano teaching in colleges and universities, mainly to improve the correction rate and enhance the ability of guiding teaching.

Application of Speech Recognition Technology in Power Grid Dispatching Automation

In recent years, with the rapid development of social economy, China’s investment in power grid dispatching automation system has also been increasing year by year to meet the increasing demand for dispatching of modern power grids. Under this background, the smart grid system characterized by information, automation, intelligence, and interaction has become the trend of future electric power development. Voice human-machine interaction technology as an advanced interactive technology has also started in the electric power intelligent dispatch system. Used in the application. This article mainly expounds the development status of intelligent dispatching system and the application of human-computer interaction technology in intelligent dispatching system. It also analyzes speech recognition technology, speech synthesis technology and typical Chinese speech recognition system in speech human-computer interaction technology, and focuses on intelligence. The design and implementation of voice human-computer interaction technology in the dispatching system, with a view to provide theoretical guidance for the development of China’s power dispatch system.

Speech Recognition Technology in Language Testing: Does Taking the Test in an English-Speaking Environment Matter?

AbstractThis study investigates a particular use of an application of speech recognition technology in the assessment of English proficiency. The use of the application, called Versant English Test, is examined in the context of a country where English is not the first language of communication, in order to determine whether or not English as the first language of the country in which the test is taken could have a bearing on the test result. As suggested by Chun (2006), this study compares the results achieved by test takers in a non-English speaking environment with those obtained by different test takers in an English speaking environment. To be able to decide whether the Versant is more prone to setting-related bias than other English proficiency tests, the Versant test scores are correlated with the TOEFL scores of the test-takers in a non-English speaking setting and the correlation coefficient is then compared with that achieved in an English-speaking environment. The results suggest that the correlation between the Versant and TOEFL in a non-English-speaking environment is not significantly different from that obtained in an English-speaking environment.

Voice Assistant — Application of Speech Recognition Technology in the Android System

This paper introduces the concept of Voice Assistant, the voice recognition service providers, several typical Voice Assistant product, and then the basic working process of the Voice Assistant is described in detail and proposed the technical bottleneck problems in the development of Voice Assistant software.

Experimental analysis of human vocal behavior: applications of speech-recognition technology.

Recent developments in speech recognition make it feasible to apply the technology to study vocal behavior. The present study illustrates the use of this technology to establish functional stimulus classes. Eight students were taught to say nonsense words in the presence of arbitrarily assigned sets of symbols consistent with three three-member experimenter-defined stimulus classes. Computer-controlled speech-recognition software was used to record, analyze, and differentially reinforce vocal responses. When the stimulus classes were established, students were taught to say a new nonsense word in the presence of one member of each stimulus class. Transfer of function was tested subsequently to determine if the novel stimulus names transferred to the remaining stimulus class members. Most subjects required two iterations of the training and testing procedures before transfer occurred. The data illustrate the usefulness of recording vocal behavior during stimulus control procedures and demonstrate the use of speech-recognition technology. The paper also describes the current state of speech-recognition technology and suggests several other areas of research that might benefit from using vocal behavior as its primary datum.

Experiments in the application of isolated-word recognition to secondary driving controls for the disabled

Adaptive driving controls such as mechanical hand controls or electromechanical contact switches are now available which allow even the most severely impaired to drive. The residual functions, however, are all directed toward primary controls such as steering, braking, and accelerating, limiting the ability to operate secondary controls such as the horn, turn signals, ignition, and headlights, etc. In this paper, we discuss the application of speech recognition technology when operating these secondary controls. The performance of a speech recognition system inside a vehicle is studied, and the types of noise that degrade the recognition accuracy are also identified. Results are presented on the degradation in recognition performance caused by engine noise, fan noise, and interfering speech.

Current military/government applications for speech recognition

This paper presents an overview of several military/government programs in which SCI Technology, Inc. has implemented and tested its speech recognition system. Included are: (1) the Speckled Trout (U.S. Air Force), (2) LHX (Light Helicopter Experimental, U.S. Army), (3) Space Shuttle (NASA), (4) Space Station, (5) AFTI F‐16, and (6) Advanced Tactical Fighter (ATF). Some programs consist of technology demonstrations, while others involve flight testing, and one, Speckled Trout, operationally installing and utilizing a system on a continual basis. In some cases, the hardware consists of a SCI Voice Control Unit (VCU‐5137) and others, a Voice Development System (VDS‐7001). For example, the Space Station application consisted of implementing a VDS in a power system monitoring and switching network in which power switches and loads could be controlled by verbal commands. In another application, two VCUs have been delivered to NASA for future Shuttle flights in which the remote controlled cameras on board will be manipulated (switched, positioned, and focused) using speech, freeing the astronauts' hands for more manually oriented tasks such as controlling the mechanical arm in the cargo bay. The LHX applications included interrogating various on‐board systems (e.g., electrical, hydraulic, transmission) by speech commands and having the system status appear on video monitors. The Speckled Trout program is the first operational speech recognition system to be installed in an aircraft as an integral component of the aircraft's systems. Three VCUs are part of an integrated radio and navigational aides control system in which the system control can be either manual or by voice commands. However, since installation several months ago, the crew reports that radio control has been carried out almost exclusively by verbal commands. The AFTI F‐16 program includes integrating a VDS as part of a flight simulator for testing and evaluation. The ATF program has only recently started and will involve integration as part of an advanced cockpit. This paper will also discuss the evolutionary process that has proven essential to the successful application of speech recognition technology into military and governmental systems of the future.

Using Your Voice: Speech Recognition Technology in Medicine and Surgery

Data entry and control of equipment are existing applications of speech recognition technology in medicine and surgery. Specialized dictation tasks, such as radiographic reporting, are feasible using isolated speech, speaker dependent, large vocabulary devices. General dictation will be possible using an automatic dictation device. Vocabulary size varies from a few to many thousand words, but most data entry, command, and control applications require 200 words or less, whereas specialized dictation tasks require about 1000 words and general dictation for a given user requires on the order of 10,000 words.