Improved Voice Activity Detection via Contextual Information and Noise Suppression
Bibliographic record
Abstract
In this paper, we develop a contextual voice activity detection (VAD) scheme which combines both contextual and frame specific information to improve detection. Unlike many VAD algorithms which assume that the cues to activity lie within the frame alone, our scheme seeks information for activity in the current as well as the neighboring frames. The new approach provides good robustness in low SNR when the speech frame is corrupted and an alternate reliable source of activity information is necessary. Further, we present a simple noise suppression scheme to enhance the VAD performance at low SNR. The noise suppressor provides spectrally reshaped signal to the VAD. Finally, we combine the contextual VAD and the noise suppression scheme with a basic detector to form a comprehensive VAD. The proposed comprehensive VAD system is tested on speech samples from the SWITCHBOARD database. Various noises under different SNRs are added to the speech signals. Experimental results show that the proposed VAD outperforms the standard algorithm ETSI AMR VAD-1.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| 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".