Speech Recognition with In-the-Ear and Behind-the-Ear Dual-Microphone Hearing Instruments
Bibliographic record
Abstract
Abstract The primary purpose of this study was to compare the overall listening benefit in diffuse noise provided by dual-microphone technology in an in-the-ear (ITE) hearing instrument to that provided by dual-microphone technology in a behind-the-ear (BTE) hearing instrument. Further, the study was designed to determine whether the use of the dual-microphone + the manufacturer's party response algorithm in the ITE and BTE hearing instruments provided listening benefit in diffuse noise over their respective omnidirectional microphone modes. Twenty-four adults with mild to moderately severe sensorineural hearing loss were evaluated while wearing binaural BTE and ITE hearing instruments. The results indicated that the dual-microphone + party response mode did provide significant benefit in diffuse noise for both the ITE (3.27 dB signal-to-noise ratio [SNR] improvement) and BTE (5.77 dB SNR improvement) hearing instruments relative to their respective conventional omnidirectional microphones. No significant difference in performance was found between the ITE and BTE hearing instruments when each device was in the dual-microphone + party response mode. It is concluded that the use of dual-microphone technology in both ITE and BTE hearing instruments can improve speech recognition in diffuse noise. Abbreviations: BTE = behind the ear, DI = Directivity Index, DSL [i/o] = desired sensation level input/output, HINT = Hearing in Noise Test, ITE = in the ear, KEMAR = Knowles electronic manikin for acoustic research, REAR = real-ear aided response, RECD = real-ear-to-coupler difference, RESR = real-ear saturation response, RTS = reception threshold for sentences, SC + a.R.T = super compression plus adaptive recovery time, SNR = signal-to-noise ratio
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.
How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".