Keyword identification framework for speech communication on construction sites
Bibliographic record
Abstract
Worksite communication is a key to boosting teamwork and improving worker performance on the construction worksite. Communication among workers on the construction site mostly consists of speech communication. However, construction sites are typically noisy due to construction tasks like drilling and operation of heavy equipment. Meanwhile, workers on construction sites typically represent a range of different ethnic and linguistic backgrounds and have different speaking accents. This can make it difficult for the listener to understand the speaker clearly, leading to miscommunication and errors in decision making on the construction site. Technological advancements in recent years can be leveraged to mitigate this problem. In this paper, a keyword identification framework is developed for speech communication on the construction site. For this framework, 12 hours of raw audio data containing 18 crane signalman speech commands (referred to as “keywords”) are collected. The crane signalman uses specific keywords to communicate with the crane operator and guide the crane operator in the crane operations. The 2-second audio clips (this being the approximate duration of each keyword) are extracted from the raw audio dataset, and construction site noise is added. Moreover, mel-frequency cepstral coefficients are extracted from the waveform audio dataset. The extracted mel-frequency cepstral coefficients, in turn, are used to train the 1-dimensional convolutional neural network. After training, the model is found to achieve a training accuracy of 97.3%, a validation accuracy of 96.1%, and a testing accuracy of 93.8%. The model is further deployed for real-time identification of keywords in speech, with the model achieving an accuracy of 95.3%. In light of these findings, it can be concluded that the developed framework is suitable for real-time application in noisy construction sites for identifying specific keywords in speech.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".