Persistent Difficulty in Noise Despite Hearing Aids: What to Reassess

Persistent Difficulty in Noise Despite Hearing Aids: What to Reassess

Persistent difficulty understanding speech in background noise is the most common reason patients report dissatisfaction with hearing aids, despite apparent compliance and “adequate” amplification in quiet.

Patients often describe this as:

• “The hearing aids work, but not in meetings”
• “Restaurants are still impossible”
• “I can hear speech, but I can’t follow it”

Recent research published in The Hearing Review (https://hearingreview.com/hearing-products/hearing-aids/speech-in-noise/leveraging-deep-neural-networks-in-hearing-aids) highlights why this remains such a challenge and what needs to be reassessed when patients continue to struggle despite amplification.

Why Hearing Aids Commonly Underperform in Noise

Traditional hearing aid fittings have focused primarily on:

• Threshold audibility
• Prescriptive gain targets
• Subjective patient feedback in quiet settings

However, research shows that in complex acoustic environments, older noise-reduction approaches often:

• Treat speech and background noise as acoustically similar
• Attenuate speech cues along with competing noise
• Increase listening effort despite adequate audibility

This explains a common clinical paradox: patients “hear” sounds but do not understand speech, particularly in group or workplace environments.

What the Research Shows Has Changed

The Hearing Review article outlines how modern hearing aids now employ deep neural networks (DNNs) – a form of artificial intelligence trained on millions of real-world listening environments.

Key findings include:

• Speech and noise are modelled differently, rather than uniformly
• Speech cues are preserved while competing noise is selectively reduced
• Improvements are most evident in high-noise, multi-speaker environments

Crucially, the research also reinforces that clinical outcomes depend on correct fitting, acoustic coupling and verification, not technology alone.

Case Example: Applying Research in Clinical Practice

A patient with moderate–severe high-frequency sensorineural hearing loss presented to Pindrop Hearing in late 2025.

Background

• Previously fitted elsewhere in 2024
• Slim RIC devices selected primarily for cosmetic reasons
• Open dome coupling despite degree/configuration of loss

Primary Complaint

• Significant difficulty in meetings and group conversation
• High listening effort and fatigue
• Reduced confidence at work

Baseline Outcome Measure

• Speech-in-noise testing: maximum score ~60% using his old hearing aids
• Patient reported speech-in-noise testing had not been part of previous assessments

Reassessment and Intervention

The reassessment followed principles aligned with the current research:

1. Functional Outcome Measurement

Speech-in-noise testing was used pre- and post-intervention to objectively measure performance, rather than relying on subjective report alone.

2. Technology Matched to Listening Demands

The patient was refitted with advanced AI-based hearing aids from Phonak, using dedicated speech-in-loud-noise processing designed for complex acoustic environments.

3. Acoustic Coupling and Verification

• Closed domes selected to restore high-frequency audibility
• Real Ear Measurements (REM) used for objective verification
• Iterative fine-tuning guided by outcome measures and patient feedback

Outcome

• Post-intervention speech-in-noise score: ~90%
• Marked reduction in listening effort
• Improved communication in meetings and at home

The patient specifically identified speech-in-noise testing and verification as key differences compared to previous care.

Referral Trigger: When to Consider Reassessment

Consider referral for reassessment if a patient reports any of the following despite wearing hearing aids:

• Persistent difficulty understanding speech in noise
• “Works in quiet but not in meetings or restaurants”
• Ongoing listening fatigue or cognitive load
• Adequate audiometric gain but poor functional outcomes
• Hearing aids selected primarily for cosmetic reasons
• No previous speech-in-noise testing
• No evidence of REM-verified fitting

In many cases, reassessment – not escalation – is the most effective next step.

Clinical Implications

This case reinforces several evidence-based principles supported by current research:

• Speech-in-noise ability should be measured objectively, not inferred
• Acoustic coupling materially affects intelligibility outcomes
• AI-based processing is most effective when correctly implemented
• REM remains essential for evidence-based hearing aid provision

Technology alone does not deliver outcomes – process does.

Our Approach at Pindrop Hearing

Gold-standard assessments routinely include:

• Speech-in-noise outcome measures
• REM-verified fittings
• Evidence-led technology selection
• Outcome-driven fine-tuning

This supports clearer communication, improved satisfaction and sustained hearing aid use.

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Harry Akram BSc (Hons), RHAD
Clinical Audiologist, Pindrop Hearing