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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.043 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".