Directions for the future of technology in pronunciation research and teaching

O’Brien, Mary Grantham; Derwing, Tracey M.; Cucchiarini, Catia; Hardison, Debra M.; Mixdorff, Hansjörg; Thomson, Ron I.; Strik, Helmer; Levis, John M.; Munro, Murray J.; Foote, Jennifer A.; Levis, Greta Muller

doi:10.1075/jslp.17001.obr

Cited by 61 publications

(45 citation statements)

References 59 publications

(45 reference statements)

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“…The question was: check the statements that fit your reasons for early abandonment of the competition. p = 0.003); between Casual and Constant (χ 2 (1) = 35.010, p < 0.001); and between Habitual and Constant (χ 2 (1) = 10.275, p = 0.001).…”

Section: Tablementioning

confidence: 98%

“…Correct pronunciation is recognized as one of the most important issues in language learning, first because it permits speech comprehensibility and intelligibility to be enhanced and second, it is a means to acquiring a native-like pronunciation [2], [3]. Due to its importance and the fact that an appropriate training could significantly improve it [4], computer-assisted (aided) pronunciation training (CAPT)…”

Section: Introductionmentioning

confidence: 99%

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Using Challenges to Enhance a Learning Game for Pronunciation Training of English as a Second Language

et al. 2020

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Learning games have a remarkable potential for education. They provide an emergent form of social participation that deserves the assessment of their usefulness and efficiency in learning processes. This study describes a novel learning game for foreign pronunciation training in which players can challenge each other. Native Spanish speakers performed several pronunciation activities during a one-month competition using a mobile application, designed under a minimal pairs approach, to improve their pronunciation of English as a foreign language. This game took place in a competitive scenario in which students had to challenge other participants in order to get high scores and climb up a leaderboard. Results show intense practice supported by a significant number of activities and playing regularity, so the most active and motivated players in the competition achieved significant pronunciation improvement results. The integration of automatic speech recognition (ASR) and text-to-speech (TTS) technology allowed users to improve their pronunciation while being immersed in a highly motivational game. INDEX TERMS Computer-assisted pronunciation training, mobile learning game, mobile application, English L2 pronunciation, challenges, motivation.

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Section: Tablementioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Using Challenges to Enhance a Learning Game for Pronunciation Training of English as a Second Language

et al. 2020

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“…Several researchers have employed Automatic Speech Recognition (ASR)-based algorithms and ASR-free features to obtain automatic measures of pathological speech quality, which have been shown to be strongly correlated with human-based measures of speech intelligibility [22][23][24][25][26]. In the language learning domain, such objective and automated approaches to measuring speech intelligibility have not been undertaken, although the need is felt there as well [27]. So far, it is not clear how exactly these ASR-based and ASR-free measures are related to properties of pathological speech that can be addressed in therapy, and whether these measures could be used to develop easy-to-use tools for clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Acoustic correlates of speech intelligibility: the usability of the eGeMAPS feature set for atypical speech

Xue

Cucchiarini

Hout

et al. 2019

8th ISCA Workshop on Speech and Language Technology in Education (SLaTE 2019)

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Although speech intelligibility has been studied in different fields such as speech pathology, language learning, psycholinguistics, and speech synthesis, it is still unclear which concrete speech features most impact intelligibility. Commonly used subjective measures of speech intelligibility based on labour-intensive human ratings are time-consuming and expensive, so objective procedures based on automatically calculated features are needed. In this paper, we investigate possible correlations between a set of objective features and speech intelligibility. Specifically, we study the usability of acoustic features in the eGeMAPS feature set for predicting phoneme intelligibility by using stepwise linear multiple regression analysis. The results showed that the acoustic features are potentially usable for predicting intelligibility. This finding may help to boost the development of automatic procedures to measure speech intelligibility with the underlying relevant acoustic phonetic characteristics. Our analysis also covers the comparison between two speech types (dysarthric and normal), and between two different types of speech material (isolated words and running text). Finally, we discuss possible avenues for future research on speech intelligibility and implications for clinical practice.

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“…IN the last few years, there has been a large availability of communication technology, a growing number of people are accessing the internet easily, and digital devices have become increasingly powerful and cheaper; this sparkled the emergence of mobile Automatic Speech Recognition (ASR) applications. Indeed, speech recognition could be used as a core component of several applications such as healthcare [1,2] or language learning [3,4]. Two approaches can be used for the deployment of mobile ASR applications, a client-based approach or a remote server-based approach.…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Based on Deep AutoEncoder for Remote Arabic Speech Recognition

Dendani

Bahi

Sari

2020

Lecture Notes in Computer Science

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Remote applications that deal with speech need the speech signal to be compressed. First, speech coding transforms the continuous waveform into a numerical form. Then, the digitized signal is compressed with or without loss of information. This transformation affects the original waveform and degrades performances for further recognition of the speech signal. Meanwhile, the transmission is another source of speech degradation. To restore the original "clean" speech, speech enhancement (SE) is widely used, and deep learning algorithms are state-of-the-art, nowadays. In this paper, the target application is a remote Arabic speech recognition system, and the aim of using SE is to improve the accuracy of the speech recognizer. For that purpose, a Deep Auto Encoder (DAE) is used. The effect of the DAE-based SE is studied through different configurations, and the performances are evaluated through accuracy. The results showed an improvement of about 3.17 between the accuracy prior to the SE and that computed with the enhanced speech.

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Directions for the future of technology in pronunciation research and teaching

Cited by 61 publications

References 59 publications

Using Challenges to Enhance a Learning Game for Pronunciation Training of English as a Second Language

Using Challenges to Enhance a Learning Game for Pronunciation Training of English as a Second Language

Acoustic correlates of speech intelligibility: the usability of the eGeMAPS feature set for atypical speech

Speech Enhancement Based on Deep AutoEncoder for Remote Arabic Speech Recognition

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