EDBT 2026 Demo / reviewers in the wild / expert
William S. Mohn Jr.
dblp:178/3805
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1971
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Speech recognition and synthesis · 33% Learning theory · 33% Representation and self-supervised learning · 33% | |
| Theoretical computer science
1 paper |
Information theory · 100% |
Topics — the 3 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
feature selection |
0.0 | 1 | 1971 | Two Statistical Feature Evaluation Techniques Applied to Speaker Identification · IEEE Trans. Computers 1971 |
Natural language and speech › Speech recognition and synthesis › speaker recognition
speaker identification |
0.0 | 1 | 1971 | Two Statistical Feature Evaluation Techniques Applied to Speaker Identification · IEEE Trans. Computers 1971 |
Machine learning › Learning theory
statistical pattern recognition |
0.0 | 1 | 1971 | Two Statistical Feature Evaluation Techniques Applied to Speaker Identification · IEEE Trans. Computers 1971 |
Methods — techniques the papers use, named apart from their topics
statistical feature evaluation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1971 | Two Statistical Feature Evaluation Techniques Applied to Speaker IdentificationabstractThe problem of identifying people based solely upon samples of their speech is viewed as a statistical pattern classification problem, emphasizing the portion of the process in which an informative, concise set of features is extracted from the speech signal. This work takes into account both the limited amount of data available in a real application and the statistical dependence among the various proposed features. In addition, the results of feature evaluation should apply to speakers not involved in the evaluation set. William S. Mohn Jr. |
IEEE Trans. Computers | 1 |