Automatic Speech Recognition Software Research Articles

PRONUNCIATION LEARNING USING AUTOMATIC SPEECH RECOGNITION

Due to limited interaction between the teacher and the student, learning a foreign language can be difficult. Language learning, in contrast to most other courses, necessitates oral practice and interactive corrective feedback, which may be unavailable with little study material and time for interactions. It might not be possible for the teacher to give each student their whole attention in a classroom setting. By enabling better and more flexible work and digitizing study materials utilizing cutting- edge signal processing techniques, modern computer technology can enhance language acquisition. The project offers an online pronunciation learning tool that follows the listen and repeat method. In order to enable foreign language learners to practice their abilities remotely and even without the teachers presence, it offers an interactive interface. The underlined text will be phonetically transcriptioned by the use of automatic speech recognition software to record and process human speech. The teacher will compare it to the prompt text. Students can hear teacher-generated speech as feedback and adjust their pronunciation until it is recognized correctly. KEYWORDS: E-Learning, Pronunciation learning, Automatic Speech Recognition

Training in new forms of human-AI interaction improves complex working memory and switching skills of language professionals.

AI-related technologies used in the language industry, including automatic speech recognition (ASR) and machine translation (MT), are designed to improve human efficiency. However, humans are still in the loop for accuracy and quality, creating a working environment based on Human-AI Interaction (HAII). Very little is known about these newly-created working environments and their effects on cognition. The present study focused on a novel practice, interlingual respeaking (IRSP), where real-time subtitles in another language are created through the interaction between a human and ASR software. To this end, we set up an experiment that included a purpose-made training course on IRSP over 5 weeks, investigating its effects on cognition, and focusing on executive functioning (EF) and working memory (WM). We compared the cognitive performance of 51 language professionals before and after the course. Our variables were reading span (a complex WM measure), switching skills, and sustained attention. IRSP training course improved complex WM and switching skills but not sustained attention. However, the participants were slower after the training, indicating increased vigilance with the sustained attention tasks. Finally, complex WM was confirmed as the primary competence in IRSP. The reasons and implications of these findings will be discussed.

Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point

Background and purposeStroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment.MethodsStroke patient data (n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI.ResultsThe Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted.ConclusionsAn ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends.Trial registrationThis study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).

Role of Assistive Technology in the Rehabilitation of Children with Hearing Impairment

Assistive technology plays a vital role in the lives of disable children. Main objective of this paper is to identify the role of assistive technology in the rehabilitation of children with hearing impairment. A self-developed questionnaire was used as a research tool and for the purpose of data collection. Due to the COVID-19 situation the data were collected with the help of google form and through social media platform. Descriptive and inferential statistics was used to analyze the data. It is observed that cochlear implant, FM system, ASR (Automatic Speech Recognition) software, loop induction system, captioning, Telecommunication devices (TDD), TV streamer, remote microphones, notification systems devices, infrared systems and amplified telephones were the frequent assistive technologies used by the children with hearing impairment which are equally helpful for children in acquiring education as well as in daily life activities. It is recommended that the better and more positive results, professional need to be aware with the new technologies and get trained with the positive attitude towards the rehabilitations of children with hearing impairment.

Design of ASR Software for Recognition of the Russian Language Variants

The article first touches on the problem of speech recognition of Russian language variants. With the development and growing (ASR) popularity of the automatic speech recognition (ASR) technology, more and more attention is now being paid to the problems related to the incompatibility of modern applications to work with non-standard language varieties. This question is especially relevant for Russian, as it is, contrary to the conservative statement about its homogeneity, represented by many forms that differ from the standard one, which generally have a wide distribution in various regions of Russia and throughout the world. The study of various aspects of the interaction of ACER algorithms with non-standard varieties of the Russian language, as well as existing approaches to creating an ASR product that can process such idioms, today seems to be an urgent direction. The aim of the work is to analyze in detail the methods for developing ASR systems capable of performing the task of recognizing and processing speech samples of speakers of Russian language forms different from the standard, which may contribute to further research on this topic. The research material is based on the software interface of the SOVA ASR application for automatic speech recognition, as well as a selection of audio recordings of speech of native speakers of the Central Asian and Ukrainian versions of Russian, and the corresponding transcription texts. The research methods such as the study and analysis of specialized literature, data collection for subsequent software processing, qualitative and quantitative analysis, and experimental data are used.

Effects of Automatic Speech Recognition Software on Pronunciation for Adults With Different Learning Styles

This study investigated how learning styles (visual/verbal) and the use of Automatic Speech Recognition (ASR) software affect English as a Second Language adult learners’ improvement during a 12-week course focusing on pronunciation. In the control group (n = 28), the teacher corrected and gave feedback on the adult learners’ pronunciation; experimental group 1 (n = 33) used dictation ASR along with peers’ correction; and experimental group 2 (n = 31) used dictation ASR alone. Their pre- and post-tests on pronunciation in reading tasks and live conversation were analyzed with their learning styles taken into account, using 2-way ANCOVA. The results suggest that learning styles made a significant difference in the pronunciation performance of the reading task in all groups. Visual style learners outperformed verbal style learners in the reading task. The combination of ASR and peer correction yielded the highest improvement in both reading tasks and live conversation.

Effects of an automatic speech recognition system with peer feedback on pronunciation instruction for adults

This study examined the difference in adults’ pronunciation performance with peer feedback and individual practice when using an automatic speech recognition (ASR) system. The same ASR software was used in both the comparison (n = 31) and the experimental group (n = 33) for 12 weeks. The participants were working adults in Taiwan. During the weekly lesson, the participants dictated a text to the ASR software, Speechnotes, on its website and then practiced misidentified words by themselves (the comparison group) or with feedback from team members (the experimental group). Learners’ pronunciation was measured in reading aloud and spontaneous conversation tasks before and after the treatment. Participants’ satisfaction and perceptions of the ease of use of the software were also assessed at the end of the course. The results revealed a significant difference in the two groups’ pronunciation post-tests, suggesting that peer feedback is more effective than individual practice for the correction of pronunciation. The experimental group was more satisfied than the comparison group with the software. However, no differences in accentedness or perception of ease of use were evident. The findings, ASR software implementation challenges, and implications for adult language learning are discussed.

Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment. Prediction of therapeutic alliance from session recordings is used as a case in point. Recordings from 1,235 sessions of 386 clients seen by 40 therapists at a university counseling center were processed using automatic speech recognition software. Machine learning algorithms learned associations between client ratings of therapeutic alliance exclusively from session linguistic content. Using a portion of the data to train the model, machine learning algorithms modestly predicted alliance ratings from session content in an independent test set (Spearman's ρ = .15, p < .001). These results highlight the potential to harness natural language processing and machine learning to predict a key psychotherapy process variable that is relatively distal from linguistic content. Six practical suggestions for conducting psychotherapy research using machine learning are presented along with several directions for future research. Questions of dissemination and implementation may be particularly important to explore as machine learning improves in its ability to automate assessment of psychotherapy process and outcome. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Assessing the accuracy of automatic speech recognition for psychotherapy

Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here we show that automatic speech recognition is feasible in psychotherapy, but further improvements in accuracy are needed before widespread use. Our HIPAA-compliant automatic speech recognition system demonstrated a transcription word error rate of 25%. For depression-related utterances, sensitivity was 80% and positive predictive value was 83%. For clinician-identified harm-related sentences, the word error rate was 34%. These results suggest that automatic speech recognition may support understanding of language patterns and subgroup variation in existing treatments but may not be ready for individual-level safety surveillance.

Google speech recognition of an English paragraph produced by Korean college students in clear or casual speech styles

These days voice models of automatic speech recognition software are sophisticated enough to process the natural speech of people without any previous training. However, not much research has reported on the use of speech recognition tools in the field of pronunciation education. This paper examined Google speech recognition of a short English paragraph produced by Korean college students in clear and casual speech styles in order to diagnose and resolve students’ pronunciation problems. Thirty three Korean college students participated in the recording of the English paragraph. The Google soundwriter was employed to collect data on the word recognition rates of the paragraph. Results showed that the total word recognition rate was 73% with a standard deviation of 9.1%. The word recognition rate of clear speech was around 77.3% while that of the casual speech amounted to 68.7%. The reasons for the low recognition rate of casual speech were attributed to both individual pronunciation errors and the software itself as shown in its fricative recognition. Various distributions of unrecognized words were observed depending on each participant and proficiency groups. From the results, the author concludes that the speech recognition software is useful to diagnose each individual or group’s pronunciation problems.

Using voice to create hospital progress notes: Description of a mobile application and supporting system integrated with a commercial electronic health record

We describe the development and design of a smartphone app-based system to create inpatient progress notes using voice, commercial automatic speech recognition software, with text processing to recognize spoken voice commands and format the note, and integration with a commercial EHR. This new system fits hospital rounding workflow and was used to support a randomized clinical trial testing whether use of voice to create notes improves timeliness of note availability, note quality, and physician satisfaction with the note creation process. The system was used to create 709 notes which were placed in the corresponding patient’s EHR record. The median time from pressing the Send button to appearance of the formatted note in the Inbox was 8.8 min. It was generally very reliable, accepted by physician users, and secure. This approach provides an alternative to use of keyboard and templates to create progress notes and may appeal to physicians who prefer voice to typing.

Captioned Recorded Lectures as a Mainstream Learning Tool

Captioned Recorded Lectures as a Mainstream Learning Tool

Mobile speech recognition software: A tool for teaching second language pronunciation

This study examines the impact of the pedagogical use of mobile automatic speech recognition software (ASR) on the acquisition of the French vowel /y/ in production and perception. The participants were 42 beginner French students with no previous training in French phonetics and exposure to speech recognition software. They were divided into three experimental groups: (1) the ASR Group used an ASR application installed on their mobile devices to complete weekly pronunciation activities, with immediate written visual (textual) feedback provided by the software; (2) the Non-ASR Group completed the same weekly pronunciation activities in individual weekly sessions with a teacher, who provided immediate oral feedback using recast and repetitions; finally, (3) the Control Group participated in weekly individual meetings “to practice their conversation skills” with a teacher, who provided no pronunciation feedback. Following a pre-test/post-test design, our findings indicate that the ASR Group outperformed the other groups in French /y/ production, but not in perception.

An Acoustics-Based Assessment of Error Patterns in English Sounds Produced by Cantonese ESL Speakers

Abstract Pronunciation problem has always been the major challenge for Cantonese ESL speakers due to discrepancy between the Cantonese and English sound inventories. Traditionally, accent identification was done by listening to the accented English produced by Cantonese speakers. This is a subjective and unreliable method for learning a second language (L2). The present study made use of automatic speech recognition (ASR) technologies in identifying the error pattern associated with accented English produced by Cantonese ESL speakers. Cantonese ESL speakers are instructed to produce a list of isolated English words and a short passage. Pattern and frequency of errors in L2 English pronunciation were objectively identified from the speech samples using ASR software. Results of error analysis were compared with what linguists predicted. Results on L2 English error pattern were discussed in relation to L1 Cantonese.

The effect of automatic speech recognition systems on speaking workload and task efficiency

Purpose :This investigation explored the speech-production behaviors associated with the use of automatic speech recognition (ASR) software for dictation of spontaneous and scripted material by individuals with and without spinal cord injury (SCI). The variables of interest in determining speaking workload and efficiency included syllables per breath group, frequency of breath groups, frequency of apnea, time needed for dictation and number of words spoken during a dictation task. Method : Twelve individuals participated, six with SCI and six able-bodied cohorts matched for age, sex and height. Subjects dictated with continuous-speech ASR, discrete-word ASR and no ASR in a spontaneous-speaking situation, as well as in a scripted speaking situation. Results : For all variables, differences amongst dictation conditions were significant. No significant differences were found between speaker groups. Dictation with both discrete-word and continuous-speech ASR resulted in a decrease in the number of syllables produced per breath group, increases in the frequency of breath groups and apnea, with differences from normal being greater for dictation with discrete-word ASR. In addition, when participants dictated with either type of ASR, the amount of time and number of words produced were significantly greater than that associated with production of the same message without ASR, requiring `more work' on the part of the speaker and ultimately reducing the efficiency with which a message was produced. Conclusions : From a human factors perspective, these results suggest that ASR software, especially discrete-word ASR, has the potential to increase energy expenditure during dictation over a prolonged period of time, thereby increasing speech workloads and the potential for overuse of the laryngeal system.

Evaluating consonant errors in automatic speech recognition programs

Many voice-enabled computer applications (e.g., voice-controlled web commands, automatic call centers) use automatic speech recognition (ASR) software that allow users to command the computer by voice. Problems occur because ASR does not exhibit 100% accuracy for all speech patterns. In preparation for future evaluation of nonprototypical voices and speech technology, this project used three subjects with prototypical (no known speech pathology) to test three commercially available ASR software programs: Voice Xpress(R), Naturally Speaking(R), and Via Voice(R). Phonetic analyses revealed consonant errors in each of the three programs. Vowel errors are not addressed in this study. Naturally Speaking(R) 4 was the most accurate of the ASR programs tested. Errors were identified at the word level and analyzed by phoneme and manner of articulation. Similarities among the consonant errors were found in each program. Tests showed stop-to-stop (e.g., /t, d, b, p, g, k/), fricative-to-fricative (e.g., /s, z, f, v, sh, zh/), and fricative-to-stop errors. Nasal to nasal exchanges (e.g., /m/ /n/) were noted for each software package in each of the initial tests. Constant errors are expected to decrease as errors are identified and disseminated among software developers and User Centered Design is employed. [Work supported by NIDRR.]

Embedded ASR: Lessons Learned Integrating Speech Control into Embedded Systems

For the most basic embedded systems, an integrator must assume that the system has no keyboard support, no mouse, and no monitor. A programmable keypad and a limited alphanumeric display are the usual interface devices. Our research looks at the new generation of powerful embedded platforms and operating systems to investigate the potential integration of automatic speech recognition (ASR) to control high-end diagnostic systems. Our goal is to present a working design model and identify critical issues to aid future designers and system engineers in the process of incorporating speech control into embedded systems. Critical issues discussed include hardware platforms, use of off-the-shelf ASR software, real-time versus near-real-time operating systems, and user system navigation and location awareness.

Evaluation of the accuracy of a continuous speech recognition software system in radiology.

ADIOLOGY REPORTS in most medical settings are generally dictated by the radiologists and then transci by a human transcriptionist, resulting in a text report. The radiologist then finalizes the transcribed report after reviewing it and assuring the accuracy of the text. Time delays between the various stages of this process usually mean that the final reports are available only after several hours or more have passed following interpretation of the examination. The emergence of automatic speech recognition software has suggested that all reading rooms operate in the direct dictation mode without involving the human transci When used in conjunction with electronic systems for managing the text information (radiology information system IRIS]) and image information (picture archiving