Incorporating Phonetic Knowledge Into an Evolutionary Subspace Approach for Robust Speech Recognition
Why this work is in the frame
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Bibliographic record
Abstract
The reliability of automatic speech recognition (ASR) systems is closely related to the parameterization process which is expected to accurately characterize the phonetic, dynamic and static components in speech. For this purpose, ASR methods build speech sound models based on large speech corpora that attempt to include common sources of variability that may occur in real-life conditions. Nevertheless, not all variabilities can reasonably be covered. For that reason, the performance of current ASR systems, whose designs are predicated on relatively noise-free conditions, degrades rapidly in the presence of high-level adverse conditions. To cope with mismatched (adverse) conditions and to achieve noise robustness, we present in this paper an original approach that operates in two steps. The first one consists of integrating in the front-end process, besides mean-subtracted mel-frequency cepstral coefficients, acoustic distinctive features that provides a more convenient interface to higher-level components of ASR systems. The second step consists of combining subspace filtering and Genetic Algorithms to get less-variant parameters. The advantages of this approach include that no estimation of noise is required and the recognition system is not modified. The effectiveness of the method is assessed in high interfering car noise by using a noisy subset of the TIMIT database. Obtained results show that the proposed method reduces drastically the word error rate for a wide range of signal-to-noise ratios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 it