On the importance of phase in human speech recognition
Bibliographic record
Abstract
In this paper, we analyze the effects of uncertainty in the phase of speech signals on the word recognition error rate of human listeners. The motivating goal is to get a quantitative measure on the importance of phase in automatic speech recognition by studying the effects of phase uncertainty on human perception. Listening tests were conducted for 18 listeners under different phase uncertainty and signal-to-noise ratio (SNR) conditions. These results indicate that a small amount of phase error or uncertainty does not affect the recognition rate, but a large amount of phase uncertainty significantly affects the recognition rate. The degree of the importance of phase also seems to be an SNR-dependent one, such that at lower SNRs the effects of phase uncertainty are more pronounced than at higher SNRs. For example, at an SNR of -10 dB, having random phases at all frequencies results in a word error rate (WER) of 63% compared to 24% if the phase was unaltered. In comparison, at 0 dB, random phase results in a 25% WER as compared to 11% for the unaltered phase case. Listening tests were also conducted for the case of reconstructed phase based on the least square error estimation approach. The results indicate that the recognition rate for the reconstructed phase case is very close to that of the perfect phase case (a WER difference of 4% on average).
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".