Text-independent distributed speaker identification and verification using GMM-UBM speaker models for mobile communications
Bibliographic record
Abstract
This paper presents the simulation results of a speaker identification and verification (SIDV) system that would be efficient for resource limited mobile devices. The proposed system works as a text-independent system within the distributed speech recognition (DSR) framework and is designed to identify a target speaker or imposter using short digit utterances rather than long utterances. In this distributed SIDV (DSIDV), the target speaker model is developed by using the most popular generative system called a GMM-UBM system. A Gaussian Mixture Model (GMM) for each true speaker is derived from the Universal Background Model (UBM) by using Bayesian maximum a posteriori (MAP) adaptation. The objective of this paper is to show how speaker recognition and verification over telephone channels can be done using short speeches and DSR technology robust to channel distortions. The ETSI Aurora2 speech corpus was tested in these experiments. The experimental results show that the proposed DSIDV system yields excellent identification and detection performances in a ETSI DSR evaluation task and would be suitable for small hand held mobile devices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".