The state of voice user interfaces in Latin America
Bibliographic record
Abstract
This paper describes and analyzes the state of research in Voice User Interfaces (VUIs) in Latin America based on the review of scientific documents published in SCOPUS from 1999 to June 2020, through a bibliometric analysis. We analyzed 419 academic papers. Although a gradual increase is observed over the years, the number of published documents has increased considerably since 2014. Brazil (44%) and Mexico (28%) are the countries with more documents published. Co-authorship occurs between Latin American countries (Brazil, Argentina, Mexico, Ecuador, and Costa Rica). However, the mayor collaboration from Latin American countries occurs with the United States, France, Germany, Spain, Portugal, the United Kingdom, and Japan. The main researched topics are studies of automatic speech recognition, artificial intelligence, speech processing, and human-computer interaction, which have grown over the past few years. Natural language processing, conversational agents, user experience, and chatbots are keywords related to more recent studies. Our analysis reveals that the primary active research developed in the short-term future are personal assistants and assistive technology using voice user interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".