Maha Saliba Foster

Teaching Professor, Arabic

What I do

I am a teaching Associate Professor of Arabic. In my teaching, I embrace the innovative integrated approach through the integration of Modern Standard Arabic and the spoken variety of the Levant. My students have a strong grounding in the written form of Arabic while learning to communicate in everyday situations in an authentic and natural way. My extensive teaching experience, my native understanding of the culture, along with my expertise in linguistics, language acquisition, and the newest pedagogical approaches to teaching a foreign language, all have contributed to creating a rigorous program in Arabic at the University of Denver. I am also working at expanding the program and finding new opportunities for study abroad and community service in the Denver area. My second year students work once a week with Arab students at South High school reading together in Arabic and developing relationships.
Additionally, I teach a Freshmen Seminar on the Tales of the Arabian Nights. These ageless stories that transport the readers into a world of magic and fantasy are also the perfect platform to discuss pervasive topics of our times such as orientalism, race and gender, stereotyping, and others. Students in this seminar are trained to re-examine their positions by considering all sides of an issue.
My research interest is in linguistics and cognitive science. I seek to find ways, including in the field of Artificial Intelligence, to help students better perceive and produce the foreign-sounding Arabic sounds.


Foreign language Linguistics Cognitive Science Language Acquisition

Professional Biography

I was born and raised in Beirut Lebanon. I attended a French school and after graduating with a French and Lebanese Baccalaureate, I majored in education at the American University of Beirut, and graduated a master degree in Applied Linguistics (TESL). At the University of Colorado in Boulder, I earned another master degree in Linguistic with a certificate in Cognitive Science before completing a joint PH.D. in Linguistics and Cognitive Science. In my spare time, I love to hike, play the piano, cook, and read. I am also an avid traveler.


  • Ph.D., Linguistics and Cognitive Science, University of Colorado at Boulder, 2016
  • MA, Linguistics, CU Boulder, 2012
  • MA, Linguistics and education, American University of Beirut, 1982

Licensure / Accreditations

  • Colorado teaching licence

Professional Affiliations

  • American Association of Teachers of Arabic
  • American Council on the Teaching of Foreign Language
  • Linguistic Society of America


Visual Speech Perception of Arabic Emphatics and Gutturals
This investigation explores the potential effect on perception of speech visual cues associated with Arabic gutturals (AGs) and Arabic emphatics (AEs); AEs are pharyngealized phonemes characterized by a visually salient primary articulation but a rather invisible secondary articulation produced deep in the pharynx. The corpus consisted of 72 minimal pairs each containing two contrasting consonants of interest (COIs), an emphatic versus a non-emphatic, or a guttural paired with another guttural. In order to assess the potential effect that visual speech information in the lips, chin, cheeks, and neck has on the perception of the COIs, production data elicited from 4 native Lebanese speakers was captured on videos that were edited to allow perceivers to see only certain regions of the face. Fifty three Lebanese perceivers watched the muted movies each presented with a minimal pair containing the word uttered in the video, and selected in a forced identification task the word they thought they saw the speaker say.
The speakers’ speech was analyzed to help explore what in their production informed correct identification of the COIs. Perceivers were above chance at correctly identifying AEs and AGs, though AEs were better perceived than AGs.
In the emphatic category, the effect on perception of measurement differences between a word and its pair was
submitted to automatic speech recognition. The machine learning models were generally successful at correctly classifying COIs as emphatic or non-emphatics across vowel contexts; the models were able to predict the probability of perceivers’ accuracy in identifying certain COIs produced by certain speakers; also, an overlap between the
measurements selected by the computer and those selected by human perceivers was found. No difference in perception of AEs according to the part of the face that was visible was observed, suggesting that the lips, present in all of the videos, were most important for perception of emphasis. Conversely, in the perception of AGs, lips were
not as informative and perceivers relied more on cheeks and chin. The presence of visible cues associated with the AEs, particularly in the lips, suggests that such visual cues might be informative for non-native learners as well, if they were trained to attend to them.