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.
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