Adaptive Pitch-Based Speech Detection for Hands-Free Applications
Bibliographic record
Abstract
This paper proposes a new algorithm for classifying an audio segment as speech or non-speech. The proposed algorithm is capable of handling reverberation and low signal-to-noise environments; therefore, it is suitable for hands-free applications. The algorithm divides an audio segment into frames, estimates the presence of pitch in each frame, and calculates a pitch ratio parameter. This parameter is then used to classify the audio segment. The threshold used in calculating this parameter is adapted to accommodate different environments. The performance of the proposed algorithm is evaluated for different signal-to-noise ratios and different segment sizes using a library of audio segments. The library includes speech segments and nonspeech segments such as fan noise and cocktail noise. Using 0.4 second segments it is shown that the proposed algorithm can achieve a correct decision for 95.7% of the speech segments and 96.7% of the nonspeech segments under reverberant conditions.
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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 | 0.000 |
| 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".