Recognition of high frequency words from speech as a predictor of L2 listening comprehension

Matthews, Joshua; Cheng, Junyu

doi:10.1016/j.system.2015.04.015

Cited by 102 publications

(105 citation statements)

References 31 publications

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“…Consequently, Weir (2005:78) suggests that item writers should always investigate whether "the input or out text require knowledge of too many unknown lexical items." While recent research (e.g., Matthews and Cheng 2015) suggests that high-frequency words only partially account for the variance in the listening comprehension scores, the negative impact of low-frequency/unknown vocabulary seems to remain undisputed. Kobeleva's (2012) study, for instance, shows that even the presence of unknown proper names in a listening comprehension text can negatively affect the performance of test-takers.…”

Section: Contextually Valid Texts: Lexical and Syntactic Aspectsmentioning

confidence: 88%

“…The C&G and the FCE, on the other hand, are similar in this respect. Since frequency and familiarity of the lexis are significant factors affecting the difficulty of the test (see Buck 2001;Kobeleva 2012;and Matthews and Cheng 2015), the results can be interpreted as showing that the GM is lexically the most demanding of the three tests analysed.…”

Section: Discussionmentioning

confidence: 99%

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Syntactic and Lexical Complexity of B2 Listening Comprehension Subtests in English: A Comparative Study

Ilc

Stopar

2016

Romanian Journal of English Studies

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Adopting Weir's (2005) socio-cognitive validation framework, the present paper focuses on the syntactic and lexical complexity of listening comprehension subtests in three B2-level examinations: The City Guilds international examination in English, The First Certificate in English, and the General Matura in English. By analysing and interpreting the results obtained from different automated tools, the research aims to determine to what extent the three subtests are comparable. The results of the study suggest the unreliability of the Common European Framework of Reference for Languages (CEFR) as a sole mechanism for test comparisons.

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Section: Contextually Valid Texts: Lexical and Syntactic Aspectsmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Syntactic and Lexical Complexity of B2 Listening Comprehension Subtests in English: A Comparative Study

Ilc

Stopar

2016

Romanian Journal of English Studies

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“…Given a speech example of N = 8 word, assume that {s(0), s(1), ⋯, s(4), s (5), s (6), s (7), s(8)} = {0, 10, 30, 60, 90, 120, 150, 180, 210}. The speech recognition task fails if the total score reaches S = 100.…”

Section: An Numerical Example Analysis Of the First Strategymentioning

confidence: 99%

“…Below, we analyse the conditions under the same threshold and different () si score arrays. The above score array { () si } = {s(0), s(1), ⋯, s(4), s (5), s (6), s (7), s(8)} = {0, 10, 30, 60, 90, 120, 150, 180, 210} is increasing, but it is increasing irregularly. Now, we analyse other three regular increments, that is, () si is a function value.…”

Section: An Numerical Example Analysis Of the Second Strategymentioning

confidence: 99%

“…Speech recognition technology has applications in different systems, such as automatic translation telephones, question and answer machines, and intelligence decisions support systems [1][2][3][4]. The mechanism of speech recognition lies in the separation of the words and the matching of patterns between the words in the speech and the words in a dictionary [5][6]. Although many measures have been put forward to improve speech recognition accuracy, quantitatively evaluating the performance and reliability of speech recognition tasks is relatively limited [7].…”

Section: Introductionmentioning

confidence: 99%

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Reliability Modeling of Speech Recognition Tasks

Qiu¹

2018

IJPE

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Speech recognition is becoming the key technology of man-machine interfaces in information technology. The application of voice technology has become a competitive and new high-tech industry. However, due to the big volume of vocabulary, continuous voice, and personalized accents, it is hard to make speech recognition completely accurate. In this paper, a reliability model is proposed to measure the performance of speech recognition. In particular, two types of task failures are suggested and an iterative approach is adopted. Numerical examples are proposed for illustrative purposes.

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Application of Memristors in Hardware Security: A Current State‐of‐the‐Art Technology

Liu

Geng

2020

Advanced Intelligent Systems

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Memristors are widely used in hardware security applications. Research progress in memristor‐based physical unclonable functions (PUFs), random number generators (RNGs), and chaotic circuits is reviewed. To enhance device security, PUFs and RNGs apply randomness of memristors and incorporate 3D crossbars to amplify the number of challenge‐response pairs and provide proof of the destruction of the key, which enables the administrator to firmly control the device information. In addition, the image encryption technique based on the chaotic system is summarized. Furthermore, an assessment of the research advancement in PUFs, RNGs, and chaotic circuits is conducted. This Review examines the characteristics, applications, progress, and challenges of memristors in hardware security and compares the benefits and limitations of different schemes as accelerators for hardware information protection.

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Recognition of high frequency words from speech as a predictor of L2 listening comprehension

Cited by 102 publications

References 31 publications

Syntactic and Lexical Complexity of B2 Listening Comprehension Subtests in English: A Comparative Study

Syntactic and Lexical Complexity of B2 Listening Comprehension Subtests in English: A Comparative Study

Reliability Modeling of Speech Recognition Tasks

Application of Memristors in Hardware Security: A Current State‐of‐the‐Art Technology

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