3,793 research outputs found
Native Language Identification on Text and Speech
This paper presents an ensemble system combining the output of multiple SVM
classifiers to native language identification (NLI). The system was submitted
to the NLI Shared Task 2017 fusion track which featured students essays and
spoken responses in form of audio transcriptions and iVectors by non-native
English speakers of eleven native languages. Our system competed in the
challenge under the team name ZCD and was based on an ensemble of SVM
classifiers trained on character n-grams achieving 83.58% accuracy and ranking
3rd in the shared task.Comment: Proceedings of the Workshop on Innovative Use of NLP for Building
Educational Applications (BEA
Machine Assisted Analysis of Vowel Length Contrasts in Wolof
Growing digital archives and improving algorithms for automatic analysis of
text and speech create new research opportunities for fundamental research in
phonetics. Such empirical approaches allow statistical evaluation of a much
larger set of hypothesis about phonetic variation and its conditioning factors
(among them geographical / dialectal variants). This paper illustrates this
vision and proposes to challenge automatic methods for the analysis of a not
easily observable phenomenon: vowel length contrast. We focus on Wolof, an
under-resourced language from Sub-Saharan Africa. In particular, we propose
multiple features to make a fine evaluation of the degree of length contrast
under different factors such as: read vs semi spontaneous speech ; standard vs
dialectal Wolof. Our measures made fully automatically on more than 20k vowel
tokens show that our proposed features can highlight different degrees of
contrast for each vowel considered. We notably show that contrast is weaker in
semi-spontaneous speech and in a non standard semi-spontaneous dialect.Comment: Accepted to Interspeech 201
Vowel duration and the voicing effect across dialects of English
The ‘voicing effect’ – the durational difference in vowels preceding voiced and voiceless consonants – is a well-documented phenomenon in English, where it plays a key role in the production and perception of the English final voicing contrast. Despite this supposed importance, little is known as to how robust this effect is in spontaneous connected speech, which is itself subject to a range of linguistic factors. Similarly, little attention has focused on variability in the voicing effect across dialects of English, bar analysis of specific varieties. Our findings show that the voicing of the following consonant exhibits a weaker-than-expected effect in spontaneous speech, interacting with manner, vowel height, speech
rate, and word frequency. English dialects appear to demonstrate a continuum of potential voicing effect sizes, where varieties with dialect-specific phonological rules exhibit the most extreme values. The results suggest that the voicing effect in English is both substantially weaker than previously assumed in spontaneous connected speech, and subject to a wide range of dialectal variability
Towards dialect-inclusive recognition in a low-resource language: are balanced corpora the answer?
ASR systems are generally built for the spoken 'standard', and their
performance declines for non-standard dialects/varieties. This is a problem for
a language like Irish, where there is no single spoken standard, but rather
three major dialects: Ulster (Ul), Connacht (Co) and Munster (Mu). As a
diagnostic to quantify the effect of the speaker's dialect on recognition
performance, 12 ASR systems were trained, firstly using baseline
dialect-balanced training corpora, and then using modified versions of the
baseline corpora, where dialect-specific materials were either subtracted or
added. Results indicate that dialect-balanced corpora do not yield a similar
performance across the dialects: the Ul dialect consistently underperforms,
whereas Mu yields lowest WERs. There is a close relationship between Co and Mu
dialects, but one that is not symmetrical. These results will guide future
corpus collection and system building strategies to optimise for cross-dialect
performance equity.Comment: Accepted to Interspeech 2023, Dubli
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