Does Speech Recognition Affect the Quality of Undergraduates' Expository Writing?

2007 ◽  
Author(s):  
Joseph Melcher
Keyword(s):  
Speech Recognition ◽  
Expository Writing

scholarly journals TEDxSK and JumpSK: A New Slovak Speech Recognition Dedicated Corpus

2017 ◽  
Vol 68 (2) ◽  
pp. 346-354
Author(s):  
Ján Staš ◽  
Daniel Hládek ◽  
Peter Viszlay ◽  
Tomáš Koctúr
Keyword(s):  
Speech Recognition ◽  
Total Duration ◽  
Principal Component ◽  
Speech Segmentation ◽  
Word Error Rate ◽  
Speech Database ◽  
Recognition Systems ◽  
Speech Transcription ◽  
Speech Segments

Abstract This paper describes a new Slovak speech recognition dedicated corpus built from TEDx talks and Jump Slovakia lectures. The proposed speech database consists of 220 talks and lectures in total duration of about 58 hours. Annotated speech database was generated automatically in an unsupervised manner by using acoustic speech segmentation based on principal component analysis and automatic speech transcription using two complementary speech recognition systems. The evaluation data consisting of 50 manually annotated talks and lectures in total duration of about 12 hours, has been created for evaluation of the quality of Slovak speech recognition. By unsupervised automatic annotation of TEDx talks and Jump Slovakia lectures we have obtained 21.26% of new speech segments with approximately 9.44% word error rate, suitable for retraining or adaptation of acoustic models trained beforehand.

PP109 Use Of Speech Recognition In Medical Reports: A Systematic Review

2018 ◽  
Vol 34 (S1) ◽  
pp. 107-107
Author(s):  
Thomas Poder ◽  
Véronique Déry ◽  
Jean-Francois Fisette
Keyword(s):  
Systematic Review ◽  
Speech Recognition ◽  
Systematic Reviews ◽  
Grey Literature ◽  
Medical Doctor ◽  
Cochrane Library ◽  
Medical Specialties ◽  
Medical Reports ◽  
End Use

Introduction:Speech recognition is increasingly used in medical reporting. The aim of this article is to identify in the literature the advantages and weaknesses of this technology, as well as barriers and facilitators to its implementation.Methods:A systematic review of systematic reviews has been conducted in PubMed, Scopus, Cochrane Library and Center for Reviews and Dissemination up to August 2017. The grey literature has also been consulted. The quality of systematic reviews has been assessed with the AMSTAR checklist. Inclusion criteria were to use speech recognition for medical reporting (front or back-end). A Survey has also been conducted in Quebec, Canada, to identify the dissemination of this technology in this province, as well as the factors of success or failure in its implementation.Results:Five systematic reviews were identified. These reviews indicated a high level of heterogeneity across studies. The quality of the studies reported was generally poor. Speech recognition is not as accurate as human transcription but can dramatically reduce the turnaround times for reporting. In front-end use, medical doctors need to spend more time for dictation and correction than with human transcription. With speech recognition, major errors can be up to three times more frequent. In back-end use, a potential increase in the productivity of transcriptionist is noted.Conclusions:Speech recognition offers some advantages for medical reporting, the main one being a reduction in turnaround times. However, these advantages are challenged by an increased burden for medical doctor and risks of additional errors in medical reports. It is also hard to identify for which medical specialties and which clinical activities the use of speech recognition will be the most beneficial.

Speech Perception Performance in Experienced Cochlear-Implant Patients Receiving the SPEAK Processing Strategy in the Nucleus Spectra-22 Cochlear Implant

1998 ◽  
Vol 41 (5) ◽  
pp. 1073-1087 ◽  
Author(s):  
Aaron J. Parkinson ◽  
Wendy S. Parkinson ◽  
Richard S. Tyler ◽  
Mary W. Lowder ◽  
Bruce J. Gantz
Keyword(s):  
Speech Recognition ◽  
Speech Perception ◽  
Cochlear Implant ◽  
Sound Quality ◽  
Learning Effects ◽  
Processing Strategy ◽  
Subjective Ratings ◽  
Open Set ◽  
Wide Range

Sixteen experienced cochlear implant patients with a wide range of speechperception abilities received the SPEAK processing strategy in the Nucleus Spectra-22 cochlear implant. Speech perception was assessed in quiet and in noise with SPEAK and with the patients' previous strategies (for most, Multipeak) at the study onset, as well as after using SPEAK for 6 months. Comparisons were made within and across the two test sessions to elucidate possible learning effects. Patients were also asked to rate the strategies on seven speech recognition and sound quality scales. After 6 months' experience with SPEAK, patients showed significantly improved mean performance on a range of speech recognition measures in quiet and noise. When mean subjective ratings were compared over time there were no significant differences noted between strategies. However, many individuals rated the SPEAK strategy better for two or more of the seven subjective measures. Ratings for "appreciation of music" and "quality of my own voice" in particular were generally higher for SPEAK. Improvements were realized by patients with a wide range of speech perception abilities, including those with little or no open-set speech recognition.

scholarly journals Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

2020 ◽  
Vol 8 (5) ◽  
pp. 1677-1681
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Signal ◽  
Short Term Memory ◽  
Long Short Term Memory ◽  
Increase In Accuracy ◽  
Two Stages ◽  
The Given ◽  
Asr System

Stuttering or Stammering is a speech defect within which sounds, syllables, or words are rehashed or delayed, disrupting the traditional flow of speech. Stuttering can make it hard to speak with other individuals, which regularly have an effect on an individual's quality of life. Automatic Speech Recognition (ASR) system is a technology that converts audio speech signal into corresponding text. Presently ASR systems play a major role in controlling or providing inputs to the various applications. Such an ASR system and Machine Translation Application suffers a lot due to stuttering (speech dysfluency). Dysfluencies will affect the phrase consciousness accuracy of an ASR, with the aid of increasing word addition, substitution and dismissal rates. In this work we focused on detecting and removing the prolongation, silent pauses and repetition to generate proper text sequence for the given stuttered speech signal. The stuttered speech recognition consists of two stages namely classification using LSTM and testing in ASR. The major phases of classification system are Re-sampling, Segmentation, Pre-Emphasis, Epoch Extraction and Classification. The current work is carried out in UCLASS Stuttering dataset using MATLAB with 4% to 6% increase in accuracy when compare with ANN and SVM.

scholarly journals The Challenges of Developing a Living Arabic Phonetic Dictionary for Speech Recognition System: A Literature Review

2021 ◽  
Vol 8 (1) ◽  
pp. 164-170
Author(s):  
Mohammad Husam Alhumsi ◽  
Saleh Belhassen
Keyword(s):  
Speech Recognition ◽  
Literature Review ◽  
Recognition System ◽  
Speech Recognition System ◽  
System A ◽  
Normal Human ◽  
Recognition Systems ◽  
Translation Systems ◽  
Arabic Speech Recognition

Phonetic dictionaries are regarded as pivotal components of speech recognition systems. The function of speech recognition research is to generate a machine which will accurately identify and distinguish the normal human speech from any other speaker. Literature affirmed that Arabic phonetics is one of the major problems in Arabic speech recognition. Therefore, this paper reviews previous studies tackling the challenges faced by initiating an Arabic phonetic dictionary with respect to Arabic speech recognition. It has been found that the system of speech recognition investigated areas of differences concerning Arabic phonetics. In addition, an Arabic phonetic dictionary should be initiated where the Arabic vowels’ phonemes should be considered as a component of the consonants’ phonemes. Thus, the incorporation of developed machine translation systems may enhance the quality of the system. The current paper concludes with the existing challenges faced by Arabic phonetic dictionary.

Spracherkennung in der manuellen Montage/Speech recognition in manual assembly

2021 ◽  
Vol 111 (09) ◽  
pp. 579-582
Author(s):  
Daniel Schulte ◽  
Martin Sudhoff ◽  
Bernd Kuhlenkötter
Keyword(s):  
Speech Recognition ◽  
Data Acquisition ◽  
Production Systems ◽  
Assembly System ◽  
Process Time ◽  
User Friendliness ◽  
Manual Assembly ◽  
Design And Testing

In diesem Beitrag wird die Konzeption und Erprobung eines Systems zur Datenerfassung mittels Spracherkennung in der manuellen Montage beschrieben. Dieses wurde in einem realen Montagesystem in der Lern- und Forschungsfabrik (LFF) des Lehrstuhls für Produktionssysteme (LPS) zur Prozesszeitaufnahme eingesetzt. Anschließend wurde die Qualität der Daten sowie auf die Bedienerfreundlichkeit untersucht. Es konnte gezeigt werden, dass die Spracherkennung eine gute Ergänzung zur manuellen Datenerfassung darstellt.   This paper describes the design and testing of a system for data acquisition using speech recognition in manual assembly. This was used in a real assembly system in the Learning and Research Factory of the Chair of Production Systems for process time recording. Subsequently, the quality of the data as well as the user-friendliness were examined. It could be shown that speech recognition is a good complement to manual data acquisition.

scholarly journals Challenges of developing a digital scribe to reduce clinical documentation burden

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Juan C. Quiroz ◽  
Liliana Laranjo ◽  
Ahmet Baki Kocaballi ◽  
Shlomo Berkovsky ◽  
Dana Rezazadegan ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Complex Nature ◽  
Clinical Settings ◽  
Clinical Documentation ◽  
Clinician Satisfaction ◽  
Clinical Environments ◽  
Medical Concepts ◽  
Medical Scribes ◽  
Topic Structure

AbstractClinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.

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