H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 31 Citations 4,379 204 World Ranking 7720 National Ranking 3608

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Statistics
  • Speech recognition

The scientist’s investigation covers issues in Speech recognition, Acoustics, Vocal tract, Speech processing and Artificial intelligence. Her work deals with themes such as Mel-frequency cepstrum and Noise, which intersect with Speech recognition. She has included themes like Consonant, Nonlinear dynamical systems and Phonation in her Acoustics study.

Abeer Alwan combines subjects such as Tongue, Formant, Speech production, Speech synthesis and Vocal folds with her study of Vocal tract. Her work in Speech processing covers topics such as Harmonic analysis which are related to areas like Infrasound, Code, Standard deviation and Speech enhancement. The Artificial intelligence study combines topics in areas such as Natural language processing, Data collection and Pattern recognition.

Her most cited work include:

  • Toward articulatory-acoustic models for liquid approximants based on MRI and EPG data. Part I. The laterals (194 citations)
  • adaptive mobile multimedia networks (185 citations)
  • Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics (171 citations)

What are the main themes of her work throughout her whole career to date?

Her primary areas of study are Speech recognition, Artificial intelligence, Acoustics, Pattern recognition and Noise. In her work, Cepstrum is strongly intertwined with Mel-frequency cepstrum, which is a subfield of Speech recognition. Her research integrates issues of Noise measurement and Natural language processing in her study of Artificial intelligence.

The various areas that Abeer Alwan examines in her Acoustics study include Tongue, American English, Speech production and Phonation. Her research in Pattern recognition intersects with topics in Phrase and Spectrogram. Her study looks at the relationship between Formant and fields such as Fundamental frequency, as well as how they intersect with chemical problems.

She most often published in these fields:

  • Speech recognition (67.64%)
  • Artificial intelligence (24.60%)
  • Acoustics (22.01%)

What were the highlights of her more recent work (between 2013-2021)?

  • Speech recognition (67.64%)
  • Artificial intelligence (24.60%)
  • Pattern recognition (18.45%)

In recent papers she was focusing on the following fields of study:

Speech recognition, Artificial intelligence, Pattern recognition, Speaker recognition and Mel-frequency cepstrum are her primary areas of study. Her Speech recognition research includes elements of Normalization and Noise. Her Artificial intelligence research includes themes of Machine learning, Noise measurement and Natural language processing.

Her research investigates the connection between Pattern recognition and topics such as Phrase that intersect with problems in Support vector machine, Segmentation and Spectrogram. Her Speaker recognition study combines topics from a wide range of disciplines, such as Speech processing, Joint probability distribution, Utterance, Mixture model and NIST. Her Mel-frequency cepstrum study also includes fields such as

  • TIMIT which is related to area like Decision rule and Speech Acoustics,
  • Pitch tracking which is related to area like Vocal tract and Vocal folds.

Between 2013 and 2021, her most popular works were:

  • Glottal source processing: From analysis to applications (59 citations)
  • Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training dataa) (30 citations)
  • Attention Based CLDNNs for Short-Duration Acoustic Scene Classification. (27 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Speech recognition

Abeer Alwan focuses on Speech recognition, Speaker recognition, Artificial intelligence, Quality and Mel-frequency cepstrum. Her Speech recognition research is multidisciplinary, relying on both Vocal folds, Glottis and Noise. The various areas that Abeer Alwan examines in her Artificial intelligence study include Natural language processing and Pattern recognition.

Her studies in Pattern recognition integrate themes in fields like Keyword spotting and Voice activity detection. As a part of the same scientific family, Abeer Alwan mostly works in the field of Mel-frequency cepstrum, focusing on TIMIT and, on occasion, Decision rule, Formant and Cepstrum. The concepts of her Speech processing study are interwoven with issues in Glottal flow, Vocal tract, Channel and Glottal closure.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

adaptive mobile multimedia networks

A. Alwan;R. Bagrodia;N. Bambos;M. Gerla.
IEEE Personal Communications (1996)

273 Citations

An articulatory study of fricative consonants using magnetic resonance imaging

Shrikanth S. Narayanan;Abeer A. Alwan;Katherine Haker.
Journal of the Acoustical Society of America (1995)

270 Citations

Steady-state analysis of continuous adaptation in acoustic feedback reduction systems for hearing-aids

M.G. Siqueira;A. Alwan.
IEEE Transactions on Speech and Audio Processing (2000)

213 Citations

Age, sex, and vowel dependencies of acoustic measures related to the voice source.

Markus Iseli;Yen-Liang Shue;Abeer Alwan.
Journal of the Acoustical Society of America (2007)

210 Citations

Toward articulatory-acoustic models for liquid approximants based on MRI and EPG data. Part I. The laterals

Shrikanth S. Narayanan;Abeer A. Alwan;Katherine Haker.
Journal of the Acoustical Society of America (1997)

198 Citations

Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics

Thomas Drugman;Abeer Alwan.
conference of the international speech communication association (2011)

197 Citations

Acoustic modeling of American English /r/.

Carol Y. Espy-Wilson;Suzanne E. Boyce;Michel Jackson;Shrikanth Narayanan.
Journal of the Acoustical Society of America (2000)

170 Citations

On the Relationship between Face Movements, Tongue Movements, and Speech Acoustics

Jintao Jiang;Abeer Alwan;Patricia A. Keating;Edward T. Auer.
EURASIP Journal on Advances in Signal Processing (2002)

150 Citations

On the use of variable frame rate analysis in speech recognition

Qifeng Zhu;A. Alwan.
international conference on acoustics, speech, and signal processing (2000)

146 Citations

A contribution to simulating a three-dimensional larynx model using the finite element method.

Marcelo de Oliveira Rosa;José Carlos Pereira;Marcos Grellet;Abeer Alwan.
Journal of the Acoustical Society of America (2003)

144 Citations

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