Academic Appointments

  • Assistant Professor, Communication

Honors & Awards

  • Bernard S. Finn IEEE History Prize, IEEE Life Members’ Fund and the Society for the History of Technology (SHOT) (2020)

Program Affiliations

  • Modern Thought and Literature
  • Science, Technology and Society

All Publications

  • Divination Engines: Natural Language Processing, Artificial Intelligence, and the Making of Algorithmic Culture Li, X. University of Chicago Press. 2026

    View details for DOI 10.7208/chicago/9780226837024.001.0001

  • History Can Help Us Chart AI's Future ISSUES IN SCIENCE AND TECHNOLOGY Li, X. 2024; 40 (2)

    View details for Web of Science ID 001330439500032

  • "There's No Data Like More Data": Automatic Speech Recognition and the Making of Algorithmic Culture OSIRIS Li, X. 2023; 38 (1): 165-182

    View details for DOI 10.1086/725132

    View details for Web of Science ID 001048067900009

  • The Measure of Meaning Automatic Speech Recognition and the Human-Computer Imagination ABSTRACTIONS AND EMBODIMENTS Li, X. edited by Abbate, J., Dick, S. 2022: 341-359

    View details for Web of Science ID 001052247400017

  • Vocal Features: From Voice Identification to Speech Recognition by Machine. Technology and culture Li, X., Mills, M. 2019; 60 (2S): S129-S160

    Abstract

    This article considers machine methods used in the collection, processing, and application of vocal recordings for speaker identification and speech recognition between 1908 and 1970. The first phonographic archives featured collections of "vocal portraits" that prompted international investigations into the essential features of human voices for individual identification. Visual records of speech later found the same applications, but as "voiceprint identification" via sound spectrography began to achieve legal and commercial success in the 1960s, the procedure attracted more widespread scientific attention, which ultimately discredited both its accuracy and its rationale. At the same time, spectrogram collections spurred a new application-speech recognition by machine. The changing status of the speech spectrogram, from a record of unique features of individual voices to a model of fundamental invariants in speech sounds, was rooted in the demands of automated processing and a corresponding shift from the sound archive to the acoustic database.

    View details for DOI 10.1353/tech.2019.0066

    View details for PubMedID 31231075