Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Keywords Extraction and Sentiment Analysis using Automatic Speech
  Recognition   Automatic Speech Recognition (ASR) is the interdisciplinary subfield of
computational linguistics that develops methodologies and technologies that
enables the recognition and translation of spoken language into text by
computers. It incorporates knowledge and research in linguistics, computer
science, and electrical engineering fields. Sentiment analysis is contextual
mining of text which identifies and extracts subjective information in the
source material and helping a business to understand the social sentiment of
their brand, product or service while monitoring online conversations.
According to the speech structure, three models are used in speech recognition
to do the match: Acoustic Model, Phonetic Dictionary and Language Model. Any
speech recognition program is evaluated using two factors: Accuracy (percentage
error in converting spoken words to digital data) and Speed (the extent to
which the program can keep up with a human speaker). For the purpose of
converting speech to text (STT), we will be studying the following open source
toolkits: CMU Sphinx and Kaldi. The toolkits use Mel-Frequency Cepstral
Coefficients (MFCC) and I-vector for feature extraction. CMU Sphinx has been
used with pre-trained Hidden Markov Models (HMM) and Gaussian Mixture Models
(GMM), while Kaldi is used with pre-trained Neural Networks (NNET) as acoustic
models. The n-gram language models contain the phonemes or pdf-ids for
generating the most probable hypothesis (transcription) in the form of a
lattice. The speech dataset is stored in the form of .raw or .wav file and is
transcribed in .txt file. The system then tries to identify opinions within the
text, and extract the following attributes: Polarity (if the speaker expresses
a positive or negative opinion) and Keywords (the thing that is being talked
about).

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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con