Artificial Intelligence-driven Speech Signal Extraction and Generation Based on Wave RNN Models
Bibliographic record
Abstract
In the era of artificial intelligence, the technology of speech conversion has developed rapidly and has gradually become a hot topic of research in the field of speech processing.This paper explores the problem of speech signal extraction and generation based on Wave RNN model, and constructs a speech conversion generation model driven by artificial intelligence.First, the short-time Fourier transform is utilized to convert and preprocess the speech signal in the time-frequency domain.Second, a stepwise speech enhancement model is proposed to enhance the perceived strength of the speech signal.Then, a speech generation model based on improved self-attention mechanism and RNN is designed to realize the generation of speech signals.Finally, the model effect is evaluated for application.The time-frequency domain feature that mixes time-domain features and frequencydomain features is able to capture the characteristics of speech signals more comprehensively than a single time-domain feature and frequency-domain feature, which corresponds to a higher recognition accuracy and a lower training loss value.Meanwhile, after speech enhancement, the average accuracy of model A~D speech recognition is improved by 19%~25%, which indicates that the stepped speech enhancement model used in this paper can substantially enhance the perceptual strength of speech signals.In addition, the language conversion model in this paper outperforms other speech conversion models in both MCD and RMSE, and its advantage in rhyme mapping is obvious, and the pitch of the output speech is more accurate and natural.The model in this paper has high practical value in speech signal generation and conversion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".