TranscribeSight: Redefining ASR Evaluation for Industry 4.0 Knowledge Capture

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.

Organizations, researchers, and software increasingly use automatic speech recognition (ASR) to transcribe speech to text. However, ASR can be less accurate for (i.e., biased against) certain demographic subgroups. This is concerning, given that the machine-learning (ML) models used to automatically score video interviews use ASR transcriptions of interviewee responses as inputs. To address these concerns, we investigate the extent of ASR bias and its effects in automatically scored interviews. Specifically, we compare the accuracy of ASR transcription for English as a second language (ESL) versus non-ESL interviewees, people of color (and Black interviewees separately) versus White interviewees, and male versus female interviewees. Then, we test whether ASR bias causes bias in ML model scores-both in terms of differential convergent correlations (i.e., subgroup differences in correlations between observed and ML scores) and differential means (i.e., shifts in subgroup differences from observed to ML scores). To do so, we apply one human and four ASR transcription methods to two samples of mock video interviews (Ns = 1,014 and 414), and then we train and test models using these different transcripts to score multiple constructs. We observed significant bias in the commercial ASR services across nearly all comparisons, with the magnitude of bias differing across the ASR services. However, the transcription bias did not translate into meaningful measurement bias for the ML interview scores-whether in terms of differential convergent correlations or means. We discuss what these results mean for the nature of bias, fairness, and validity of ML models for scoring verbal open-ended responses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

This paper explores the human-factors issues in designing human/machine interfaces employing automatic speech recognition (ASR) technology. It describes how a designer of a product or service who uses this technology must ensure successful interactions between computers and end-users, leading to customer satisfaction and success of the product or service. The paper discusses how ASR technology can support human/machine interactions in telecommunications applications, if the human-factors issues that arise in ASR user-interface (UI) design can be appropriately addressed.

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

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.

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.

In order to improve the performance of the speech recognition and pronunciation quality evaluation system, the deep learning-based computer aided foreign language pronunciation evaluation and learning system has become a research focus of current artificial intelligence technology. Combining with the current advanced voice information technology theory, based on the previous research results, proposed to sparse since the encoder deep learning neural network is applied to speech recognition, using sparse automatic encoder based on MFCC features in-depth study, the imitation of the auditory nerve sparse touches the depth of feature extracting signal, beneficial to the improvement of the HMM model for speech recognition accuracy, meet the needs of the current computer assisted English teaching. The simulation results show that the recognition rate of the deep learning neural network is obviously superior to that of the traditional speech recognition algorithm, which realizes more accurate human-computer interaction and improves the reliability of the evaluation of the quality of foreign language pronunciation.

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.

Automatic Speech Recognition (ASR) systems are increasingly powerful and more accurate, but also more numerous with several options existing currently as a service (e.g. Google, IBM, and Microsoft). Currently the most stringent standards for such systems are set within the context of their use in, and for, Conversational AI technology. These systems are expected to operate incrementally in real-time, be responsive, stable, and robust to the pervasive yet peculiar characteristics of conversational speech such as disfluencies and overlaps. In this paper we evaluate the most popular of such systems with metrics and experiments designed with these standards in mind. We also evaluate the speaker diarization (SD) capabilities of the same systems which will be particularly important for dialogue systems designed to handle multi-party interaction. We found that Microsoft has the leading incremental ASR system which preserves disfluent materials and IBM has the leading incremental SD system in addition to the ASR that is most robust to speech overlaps. Google strikes a balance between the two but none of these systems are yet suitable to reliably handle natural spontaneous conversations in real-time.

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>

In this paper, we describe the work developed in the creation of syllable-based language model for continuous speech recognition system for Myanmar language. Speech recognition systems contain language model as the part of prediction word order sequence. In English and other languages, speech recognition system can use word-based language model. Since Myanmar is monosyllabic and syllable-timed language, the syllable-based language model is more suitable in speech recognition system. At the first step, this paper explains the structure of traditional (Phrase based) language model and describes how to normalized Myanmar sentences and building of normalized Myanmar language model. In the second step, the evaluation of the two language models is expressed. Finally, the comparison of these two language models with the perplexity values is described. The syllable-based Myanmar language model has lower perplexity value than the traditional language model. This language model can support the creation of word-based Myanmar language model.

Flask-based ASR for Automated Disorder Speech Recognition