Auditory-based acoustic distinctive features and spectral cues for automatic speech recognition using a multi-stream paradigm
Bibliographic record
Abstract
In this paper, a multi-stream paradigm is proposed to improve the performance of automatic speech recognition (ASR) systems. Our goal in this paper is to improve the performance of the HMM-based ASR systems by exploiting some features that characterize speech sounds based on the auditory system and one based on the Fourier power spectrum. It was found that combining the classical MFCCs with some auditory-based acoustic distinctive cues and the main peaks of the spectrum of a speech signal using a multi-stream paradigm leads to an improvement in the recognition performance. The Hidden Markov Model Toolkit (HTK) was used throughout our experiments to test the use of the new multi-stream feature vector. A series of experiments on speaker-independent continuous-speech recognition have been carried out using a subset of the large read-speech corpus TIMIT. Using such multi-stream paradigm, N-mixture mono-/tri-phone models and a bigram language model, we found that the word error rate was decreased by about 4.01%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".