Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Speaker Fluency Level Classification Using Machine Learning Techniques   Level assessment for foreign language students is necessary for putting them
in the right level group, furthermore, interviewing students is a very
time-consuming task, so we propose to automate the evaluation of speaker
fluency level by implementing machine learning techniques. This work presents
an audio processing system capable of classifying the level of fluency of
non-native English speakers using five different machine learning models. As a
first step, we have built our own dataset, which consists of labeled audio
conversations in English between people ranging in different fluency
domains/classes (low, intermediate, high). We segment the audio conversations
into 5s non-overlapped audio clips to perform feature extraction on them. We
start by extracting Mel cepstral coefficients from the audios, selecting 20
coefficients is an appropriate quantity for our data. We thereafter extracted
zero-crossing rate, root mean square energy and spectral flux features, proving
that this improves model performance. Out of a total of 1424 audio segments,
with 70% training data and 30% test data, one of our trained models (support
vector machine) achieved a classification accuracy of 94.39%, whereas the other
four models passed an 89% classification accuracy threshold.

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The categories of this paper are: nlin
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The categories of this paper are: eess
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an