GRAMMATICAL ERROR DETECTION OF IELTS SPOKEN ENGLISH
GRAMMATICAL ERROR DETECTION OF IELTS SPOKEN ENGLISH
Automatic language assessment and learning systems are required to
support the global growth in English language learning. They need to be
able to provide reliable and meaningful feedback to help learners
develop their skills. This paper considers the question of detecting
“grammatical” errors in non-native spoken English as a first step to
providing feedback on a learner’s use of the language. A stateof-the-art
deep learning based grammatical error detection (GED) system designed
for written texts is investigated on free speaking tasks across the full
range of proficiency grades with a mix of first languages (L1s). This
presents a number of challenges. grammatical error detection of ielts spoken english
Free speech contains disfluencies that
disrupt the spoken language flow but are not grammatical errors. The
lower the level of the learner the more these both will occur which
makes the underlying task of automatic transcription harder. The
baseline written GED system is seen to perform less well on manually
transcribed spoken language. When the GED model is fine-tuned to free
speech data from the target domain the spoken system is able to match
the written performance. Given the current state-of-the-art in ASR,
however, and the ability to detect disfluencies grammatical error
feedback from automated transcriptions remains a challenge.
Automatic systems that enable assessment and feedback of learners of a
language are becoming increasingly popular. One important aspect of
these systems is to provide reliable, meaningful feedback to learners on
errors they are making. This feedback can then be used independently,
or under the supervision of a teacher, by the learner to improve their
proficiency. A growing number of applications are available to
non-native learners to improve their English speaking skills by
providing feedback on aspects such as pronunciation and fluency.
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