Serving Customers With Automatic Speech Recognition-Human-Factors Issues

PurposeCutting-edge automatic speech recognition (ASR) technology holds significant promise in transcribing and recognizing medical information during patient encounters, thereby enabling automatic and real-time clinical documentation, which could significantly alleviate care clinicians’ burdens. Nevertheless, the performance of current-generation ASR technology in analyzing conversations in noisy and dynamic medical settings, such as prehospital or Emergency Medical Services (EMS), lacks sufficient validation. This study explores the current technological limitations and future potential of deploying ASR technology for clinical documentation in fast-paced and noisy medical settings such as EMS.MethodsIn this study, we evaluated four ASR engines, including Google Speech-to-Text Clinical Conversation, OpenAI Speech-to-Text, Amazon Transcribe Medical, and Azure Speech-to-Text engine. The empirical data used for evaluation were 40 EMS simulation recordings. The transcribed texts were analyzed for accuracy against 23 Electronic Health Records (EHR) categories of EMS. The common types of errors in transcription were also analyzed.ResultsAmong all four ASR engines, Google Speech-to-Text Clinical Conversation performed the best. Among all EHR categories, better performance was observed in categories “mental state” (F1 = 1.0), “allergies” (F1 = 0.917), “past medical history” (F1 = 0.804), “electrolytes” (F1 = 1.0), and “blood glucose level” (F1 = 0.813). However, all four ASR engines demonstrated low performance in transcribing certain critical categories, such as “treatment” (F1 = 0.650) and “medication” (F1 = 0.577).ConclusionCurrent ASR solutions fall short in fully automating the clinical documentation in EMS setting. Our findings highlight the need for further improvement and development of automated clinical documentation technology to improve recognition accuracy in time-critical and dynamic medical settings.

Speech disorders are human disabilities widely present in young population but also adults may suffer from such disorders after some physical problems. In this context, the detection and further the correction of such disabilities may be handled by Automatic Speech Recognition (ASR) technology. The first works on the speech disorders detection began early in the 70s and seem to follow the same evolution as those on the ASR. Indeed, these early works were more based on the signal processing techniques. Progressively, systems dealing with speech disorders incorporate more ideas from ASR technology. Particularly, Hidden Markov Models, the state-of-the-art approaches in ASR systems, are used. This chapter reviews systems that use ASR techniques to evaluate pronunciation of people who suffer from speech or voice impairments. The authors investigate the existing systems and present the main innovation and some of the available resources.

EEG-dependent automatic speech recognition using deep residual encoder based VGG net CNN

This research paper presents a study on an English speaking practice system that utilizes automatic speech recognition (ASR) technology. The system aims to assess pronunciation accuracy and provide real-time feedback to learners, ultimately enhancing their spoken English skills. The system employs a web-based platform where users can record their speech, which is then uploaded to the server for recognition using a pre-trained ASR model. The recognized speech is compared with a reference text, allowing for the calculation of pronunciation accuracy and the generation of feedback highlighting correctly and incorrectly pronounced words. The system's effectiveness is validated through experimentation. The results demonstrate the system's high accuracy in speech recognition and the effectiveness of the feedback provided to learners. This study improves the effectiveness of language learning and can be extended to various educational environments and online language learning platforms.

Purpose To showcase how applications of automatic speech recognition (ASR) technology could help solve challenges in speech-language pathology practice with children with communication disability, and contribute to the realisation of the Sustainable Development Goals (SDGs). Result ASR technologies have been developed to address the need for equitable, efficient, and accurate assessment and diagnosis of communication disability in children by automating the transcription and analysis of speech and language samples and supporting dual-language assessment of bilingual children. ASR tools can automate the measurement of and help optimise intervention fidelity. ASR tools can also be used by children to engage in independent speech production practice without relying on feedback from speech-language pathologists (SLPs), thus bridging the long-standing gap between recommended and received intervention intensity. These innovative technologies and tools have been generated from interdisciplinary partnerships between SLPs, engineers, data scientists, and linguists. Conclusion To advance equitable, efficient, and effective speech-language pathology services for children with communication disability, SLPs would benefit from integrating ASR solutions into their clinical practice. Ongoing interdisciplinary research is needed to further advance ASR technologies to optimise children’s outcomes. This commentary paper focusses on industry, innovation and infrastructure (SDG 9) and partnerships for the goals (SDG 17). It also addresses SDG 1, SDG 3, SDG 4, SDG 8, SDG 10, SDG 11, and SDG 16.

Human computer interaction (HCI) is very crucial in our day-to-day activity. Speech is one of the essential and intuitive ways to interact with machines such as Smartphone, which has multiple sensors as microphone, camera, etc. An efficient performance speech recognition system improves interaction between man and machines by making latter more receptive to user needs. Such system has Automatic speech recognition (ASR) engine, which is facing a unique challenge of accuracy in recognition rate. By integrating acoustic signal feature vectors with the visual features, a more robust audiovisual speech recognition engine (AVSR) could be developed for real environmental scenarios. This paper presents past research and development in the field of ASR and AVSR technologies. It describes key technological perspective and admiration of the fundamental progress in ASR and AVSR. The objective of this review is to summarize and compare some of the well-known methods experimented by previous researchers, and to conclude with direction on future research proficiency in HCI system using ASR and AVSR engine.

The last few years have brought significant advances in Automatic Speech Recognition (ASR) technology due to the rise of deep learning technology. The paper discusses various challenges relating to memory usage and computing power required for ASR are discussed as well as novel methods for combating them. It also highlights the implementation of ASR in embedded devices in the medical field. It discusses its diagnostics capabilities for various neurodegenerative disorders and its role in improving life for those afflicted with deafness or dyslexia through cochlear implants and reading therapy. Despite significant improvements in such medical technology, there are still challenges such as lack of availability of data or noisy environments with reverberant conditions.

The potentially enormous audio resources now available to both organizations, and on the Internet, present a serious challenge to audio browsing technology. In this paper we outline a set of techniques that can be used to determine high level dialog structure without the requirement of resource intensive automatic speech recognition (ASR). Using syllable finding algorithms based on band pass energy together with prosodic feature extraction, we show that a sub-lexical approach to prosodic analysis can out-perform results based on ASR and even those based on a word alignment which requires a complete transcription. We consider how these techniques could be integrated into ASR technology and suggest a framework for extending this type of sub-lexical prosodic analysis.

Automatic speech recognition (ASR) has gained wide popularity in last decade. Various devices like mobile phones, computers, vehicles, and audio/video players are now being equipped with ASR technology. The increasing use and dependence on ASR technology leads to research enhancements and opportunities in this domain. This chapter provides a detailed review of various advancements in ASR systems development. It highlights history of speech recognition followed by detailed insight into recent advancements and industry leaders providing latest solutions. ASR framework has been discussed in detail which includes feature extraction techniques, acoustic modeling techniques, and language modeling techniques. The chapter also lists various popular data sets available and discusses generation of new data sets. This work will be helpful for the researchers who are new to this field and are exploring development of new speech recognition techniques.

<p>In this paper, we report on a two-part experiment aiming to assess and compare the performance of two types of automatic speech recognition (ASR) systems on two different computational platforms when used to augment dictation workflows. The experiment was performed with a sample of speakers of three major languages and with different linguistic profiles: non-native English speakers, non-native French speakers, and native Spanish speakers. The main objective of this experiment is to examine ASR performance in translation dictation (TD) and medical dictation (MD) workflows, without manual transcription vs. with transcription. We discuss the advantages and drawbacks of a particular ASR approach on different computational platforms when used by various speakers of a given language, who may have different accents and levels of proficiency in that language, and who may have different levels of competence and experience dictating large volumes of text and using ASR technology. Lastly, we enumerate several areas for future research.</p>

Flask-based ASR for Automated Disorder Speech Recognition

With the rapid development of the gaming industry and the increasing popularity of multiplayer online games, realtime voice communication has become a crucial tool for team collaboration and tactical exchanges. Automatic Speech Recognition (ASR) technology plays a vital role in modern gaming by converting voice commands into text, enabling efficient communication among players. However, existing general-purpose ASR systems face significant challenges in gaming scenarios due to the unique characteristics of in-game communication, such as short phrases, rapid speech, game-specific jargon, and environmental noise. These limitations often lead to frequent recognition errors, increasing communication costs and negatively impacting the overall gaming experience. Furthermore, the scarcity of domainspecific ASR training data exacerbates these issues, hindering system optimization. To address the challenges of ASR systems in gaming scenarios, this study proposes the GO-AEC (Gaming-Oriented ASR Error Correction) framework. The framework leverages the generative capabilities of large language models (LLMs) and employs Retrieval-Augmented Generation (RAG) techniques with a gamespecific knowledge base to better adapt to gaming environments. Additionally, we introduce a data augmentation strategy that combines LLMs with text-to-speech (TTS) techniques to enhance the diversity and robustness of game-specific datasets. The GO-AEC framework consists of three key modules: the data augmentation module, the N-best hypothesis-based LLM correction module, and the dynamic knowledge base module powered by RAG. Experimental results demonstrate that, compared to baseline methods, the proposed framework reduces the character error rate (CER) by 6.22 % and the sentence error rate (SER) by 29.71 %. These findings indicate that the GO-AEC framework effectively addresses the challenges of ASR error correction in gaming scenarios.

The present study investigated the effectiveness of using automatic speech recognition (ASR) technology to improve the pronunciation accuracy of English education majors in a Comprehensive English course. A total of 20 freshmen participated voluntarily in a 5-week quasi-experiment, all of whom belonged to the experimental group and were taught by the same teacher. In each task, all participants were required to read a short passage using an ASR-based mobile application, specifically iFlytek Voice Input. The practice pattern followed was "read -identify mistakes -correct mistakes -reread", and screenshots of the transcribed text of their speeches were saved to record their self-correction process. Results of five follow-up tests utilizing the FiF Speaking Training application showed an overall improvement in the automatic scoring of their pronunciation accuracy. This study aims to provide EFL learners with a more convenient and autonomous way to enhance their oral accuracy and contribute to the development of pedagogical practices in EFL pronunciation instruction.

In the era of intelligent healthcare, the level of hospital informatization is related to the application of electronic medical records. The electronic medical record system is the electronic recording of patient information, which is currently mainly entered manually by doctors, examiners, and other methods, which is time-consuming, labor-intensive, and inefficient. The application of speech recognition technology in electronic medical record systems can effectively improve the generation of clinical electronic medical records. This article presents an intelligent speech generation system for electronic medical records based on automatic speech recognition and synthesis technology. Through speech recognition and synthesis technology, using tools such as Pyaudio, AipSpeech, Jieba, and Wordcloud in Python programs, the system utilizes computer voice input devices to convert real-time speech content between patients and voice interaction agents into text. The electronic medical records are automatically generated using Python Docx tools, and data analysis tools are used to extract keywords from the electronic medical records, generate word clouds, and achieve intuitive visualization of medical record data. This system utilizes spoken language processing technology to implement an intelligent voice generation system for electronic medical records, aiming to save doctors the tedious process of manually entering medical records and enable them to focus more energy on communicating with patients during consultations. Helps reduce the workload of doctor information input, optimize patient communication, improve doctor diagnostic efficiency, and enhance patient experience.

Speech recognition is an important problem of pattern recognition area. It has gained popularity with the advancements in technology. Therefore, large number of researches has been done in recent years to achieve the human-like accuracy in this field. Arabic speech was neglected in past years due to inherent difficulty and complexity in it. In recent years, many researchers worked on it and most of them focused on Qur'anic database as its being the standard of formal Arabic. Need is felt for an automatic Qur'an recognizer which could help Muslims to memorise Qur'an without human involvement. This paper focuses on this issue and identifies the major differences between simple Automatic Speech Recognition (ASR) system and a language tutor using ASR technology with respect to Qur'an memorisation. The system is implemented using ASR technique which has proven to be incomplete method for an intelligent Qur'an tutor and thus need modification to accomplish such task.