Robust speech/non-speech discrimination based on pitch estimation for mobile robots
Bibliographic record
Abstract
To be used on a mobile robot, speech/non-speech discrimination must be robust to environmental noise and to the position of the interlocutor, without necessarily having to satisfy low-latency requirements. To address these conditions, this paper presents a speech/non-speech discrimination approach based on pitch estimation. Pitch features are robust to noise and reverberation, and can be estimated over a few seconds. Results suggest that our approach is more robust compared to the use of Mel-Frequency Cepstrum Coefficients with Gaussian Mixture Models (MFCC-GMM) under high reverberation levels and additive noise (with an accuracy above 98% with a latency of 2.21 sec), which makes it ideal for mobile robot applications. The approach is also validated on a mobile robot equipped with a 8-microphone array, using speech/non-speech discrimination based on pitch estimation as a post-processing module of a localization, tracking and separation system.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".