Technology & Innovation

A hearing aid may be sold in several countries with the same hardware, yet its users do not all listen to the same languages, voices or environments. Speech-processing performance that is acceptable in one test set cannot automatically be assumed to transfer to every language.

For OEM and private-label buyers, localization should therefore extend beyond translating the package and App. The validation plan should consider the acoustic cues that carry meaning in the target language and the real noise conditions in which users communicate.

Speech processing is also language processing

Languages use acoustic information differently. Vowel and consonant patterns matter across languages, but rhythm, duration and pitch may carry different levels of meaning. In tonal languages such as Mandarin, changes in fundamental-frequency contour can distinguish words.

A noise-reduction system is intended to reduce competing sound while maintaining useful speech information. If processing changes an important cue, a general improvement in comfort or noise level may not tell the complete story. Validation should ask whether the information needed by the target listener remains available.

What the new Mandarin study examined

A 2026 study in the American Journal of Audiology evaluated categorical perception of Mandarin Tones 1 and 2. Twenty adults with normal hearing and 20 adults with hearing loss were enrolled; 19 listeners with hearing loss were included in the final analysis after training criteria were applied.

The hearing-aid users were tested with commercial prescription devices in two conditions: deep-neural-network noise reduction enabled and disabled, with directional microphone noise reduction retained in the comparison setting. Participants identified stimuli along a flat-to-rising tone continuum in quiet and in cafeteria noise at 0 dB and −5 dB signal-to-noise ratios.

Performance declined as the noise condition became more difficult. With DNN processing enabled, tone-identification patterns were closer to those of the normal-hearing comparison group under the studied conditions. However, the direct DNN-on versus DNN-off difference was not statistically significant at the overall group level. Individual patterns at 0 dB SNR provided additional evidence that some listeners maintained categorical perception with DNN enabled.

What the study does not prove

The research tested a specific commercial prescription platform, two Mandarin tones, synthesized vowel stimuli, a limited participant group and two cafeteria-noise conditions. It does not establish performance for every Mandarin word, every tonal language, every noise environment or every hearing aid.

It also does not prove that any device labeled “AI” or “DNN” will preserve linguistic cues. Algorithms, training data, microphones, fitting, acoustics and test methods differ. The value for an OEM buyer is the validation question the study raises—not permission to transfer its outcome to another product.

Tomore’s current products are not represented in this article as DNN or AI hearing aids, and the cited study is not evidence for Tomore product performance.

Translate market needs into a test plan

Begin with the people and markets the product is intended to serve. List the primary languages, regional accents, common listening situations and typical support channels. Identify whether users are expected to self-adjust through preset programs, physical controls or an App.

Then define questions the test must answer. Does noise reduction preserve speech cues at several input levels? Do directional settings remain usable when talkers move? Are male and female voices represented? Can users understand program names and recover from an incorrect setting?

A single English sentence test or internal listening demonstration is not a multilingual validation program.

Preserve the cues that carry meaning

For tonal-language markets, testing should include pitch contours and natural speech material selected with qualified language and audiology specialists. Other languages may require attention to different consonant contrasts, timing patterns or frequency regions.

Do not assume that making speech louder preserves every cue. Gain, compression, frequency response, output limiting and noise processing interact. A setting that improves one metric may change another, so buyers should define both benefit and non-degradation criteria.

Use realistic talkers and noise

Laboratory repeatability matters, but the test set should also reflect daily communication. Include several native talkers, ages and speaking styles. Use relevant noise such as restaurants, family gatherings, traffic or public transport rather than one generic noise track.

Test more than one signal-to-noise ratio and document speaker locations. Very difficult conditions can reveal limits, while moderate conditions may show differences that disappear at an extreme. Record exactly which firmware, program, gain, dome, receiver and microphone configuration was used.

Separate electroacoustic verification from listening validation

Electroacoustic measurements verify output, gain, frequency response, distortion and other defined characteristics. They are necessary for production control and regulatory evidence, but they do not replace language-specific speech testing.

Likewise, user listening results do not replace objective measurements. A strong plan uses both: repeatable technical verification for the device and carefully designed perceptual evaluation for the intended listener and use case.

Localize controls, instructions and support

Users need clear program names, adjustment guidance and troubleshooting in their language. A translated term should describe what the control actually does rather than promising an outcome such as “perfect speech.” App text, packaging, instructions and customer-support scripts should refer to the same released functions.

Train support staff to capture the listening situation, language, program and device configuration when a user reports poor clarity. That information can help distinguish fit, setting, environment and product issues.

OEM multilingual-validation checklist

• Identify target languages, regions, accents and daily listening settings.
• Define the linguistic cues and speech materials that matter.
• Use qualified native-language and audiology specialists.
• Test multiple talkers, noise types and signal-to-noise ratios.
• Record firmware, program, acoustics and fitting configuration.
• Evaluate benefit and possible degradation of important cues.
• Combine electroacoustic measurements with perceptual testing.
• Localize controls, instructions and support—not only packaging.
• Keep study conclusions tied to the exact product and conditions tested.
• Do not transfer competitor or prescription-device results into private-label claims.

Tomore’s digital product platform can be evaluated with qualified partners against the exact model, configuration and target-market requirements. Language-specific performance claims should be made only after appropriate testing of the released product.