We motivate and present a corpus of scripted and spontaneous speech in both the native and the non-native language of talkers from various language backgrounds. Using corpus recordings from 11 native English and 11 late Mandarin-English bilinguals we compared speech timing across native English, native Mandarin, and Mandarin-accented English. Findings showed similarities across native Mandarin ...

This paper describes the development and validation of a new model and questionnaire to measure Iranian English as a foreign language learners’ attitudes towards the use of native versus non-native English language norms. Based on a comprehensive review of the related literature and interviews with domain experts, five factors were identified. A draft version of a questionnaire based on those f...

AIMS To test the hypothesis that exposure to ambient language in the womb alters phonetic perception shortly after birth. This two-country study aimed to see whether neonates demonstrated prenatal learning by how they responded to vowels in a category from their native language and another non-native language, regardless of how much postnatal experience the infants had. METHOD A counterbalanc...

In this research, we explored the effect of noise interruption rate on speech intelligibility. Specifically, we used the Hearing In Noise Test (HINT) procedure with the original HINT stimuli (English) and Igbo stimuli to assess speech reception ability in interrupted noise. For a given noise level, the HINT test provides an estimate of the signal-to-noise ratio (SNR) required for 50%-correct sp...

This paper describes our results at the NLI shared task 2017. We participated in essays, speech, and fusion task that uses text, speech, and i-vectors for the task of identifying the native language of the given input. In the essay track, a linear SVM system using word bigrams and character 7-grams performed the best. In the speech track, an LDA classifier based only on i-vectors performed bett...

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. In addition to being one of the largest corpora available for the task of NLI, at 433k examples, this corpus improves upon available resources in its coverage: it offers data from ten distinct genr...

This paper describes LIMSI’s participation to the first shared task on Native Language Identification. Our submission uses a Maximum Entropy classifier, using as features character and chunk n-grams, spelling and grammatical mistakes, and lexical preferences. Performance was slightly improved by using a twostep classifier to better distinguish otherwise easily confused native languages.

We describe the submissions entered by the National Research Council Canada in the Native Language Identification Shared Task 2017. We mainly explored the use of voting, and various ways to optimize the choice and number of voting systems. We also explored the use of features that rely on no linguistic preprocessing. Long ngrams of characters obtained from raw text turned out to yield the best ...

In this paper we illustrate how information on behaviour can be captured from natural language with the RADD-NLI. The behavioural information contained in verbal descriptions of processes is transformed into an abstract process model of characteristic processes. This abstract process model is then integrated into the knowledge base of the RADD-NLI. Although taken from a speciic domain this mode...

Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is typically framed as a classification task where the set of L1s is known a priori. Two previous shared tasks on NLI have been organized where the aim was to identify the L1 of learners of English based on essays (2...