Business 04
This is not about "adding regional voices" or "supplementing elderly speech." We design and generate the geographic, generational, and social variability inherent in Japanese as learnable structure for AI systems.
Japanese breaks AI systems not simply because "there are many dialects." It is because the following coexist simultaneously:
Regional differences in phonology and intonation
Generational differences in vocabulary, speaking rate, and sentence structure
Meaning interpretation that depends on context more than grammar
Strong coupling with non-verbal elements (pauses, silence, implication)
Expression variation based on social relationships (hierarchy, in-group/out-group)
Most existing data is created with these elements intentionally stripped away.
Common Japanese speech and language data has the following biases:
Excessive concentration on urban speakers (Tokyo metropolitan area, younger demographics)
Predominantly read speech, with little natural conversation
Age differences not considered
Dialects treated as "exceptions"
Non-verbal elements removed
As a result, models become:
Strong on standard Japanese
Weak on elderly and regional speakers
Mistiming conversational pauses
Sharply degraded performance in real deployment
M9 STUDIO rebuilds these premises from the ground up.
Principle
M9 STUDIO takes the position that dialect and age variation are not noise, but fundamental components of the language system.
Therefore, we determine at the design stage:
Which regional differences to include
Which generational differences to include
Which differences to fix, which to vary
How to use for comparison, learning, and evaluation
These cannot be decided after recording.
4.1 Regional Selection
Nationwide coverage as target
Urban/non-urban bias eliminated
Actual usage speakers, not "representative dialect" samples
4.2 Treatment of Dialects
Dialect treated as structural difference, not "label"
Phonological changes, intonation, sentence-final variations preserved
No normalization to standard Japanese
When necessary, we design as continuous quantities:
Dialect intensity
Code-mixing degree (standard + dialect)
5.1 Target Age Groups
Children
Young Adults
Middle-Aged
Elderly
Intentionally separated in design.
5.2 Treatment of Age Differences
Age differences are not merely voice quality differences. The following change:
Speaking rate
Vocabulary selection
Sentence length
Pause patterns
Frequency of implication and ellipsis
These are explicitly retained as speaker attributes and session conditions.
Japanese structure changes depending on who you're speaking to.
M9 STUDIO parameterizes:
Speaker Relationship: First meeting / familiar / hierarchical
Formality: Polite / casual
Setting: Public / private
This enables generation of differently structured data with: same speaker, same text content.
Dialect and age differences are inseparable from non-verbal elements.
Elderly speakers use longer pauses
Dialect speakers use more implication
Backchannel frequency varies by generation
We design and record dialect, age, and non-verbal elements simultaneously.
8.1 Metadata Items
Region
Age range
Dialect influence level
Context conditions
Non-verbal characteristics
8.2 Purpose
Condition control during training
Segmentation during evaluation
Bias analysis
Root cause identification in deployment
This business is selected for the following cases:
Dialogue AI requiring elderly user support
AI for local governments and public services
Nationwide robot/device deployment
Robustification of Japanese foundation models
Countermeasures for real-deployment performance degradation
Nationwide Recording
Infrastructure for new recording at national scale
Dual-Axis Design
Generation × region designed simultaneously
Non-Verbal Integration
Non-verbal elements handled without separation
Reproducible Regeneration
Can regenerate under the same conditions
Long-Term Rights
Rights and consent structure durable for long-term use
M9 STUDIO's Japanese dialect and age-stratified data business is not about "averaging" Japanese — it is about passing Japanese to AI without breaking it.
This is not suited for: short-term accuracy improvements or temporary demos.
However, for those who want to build Japanese AI that works nationwide, across all ages, in production — we can support from design to execution.
Our team brings decades of experience in Japanese language processing, SEO, and linguistic research across major Japanese enterprises.
We maintain proprietary Japanese linguistic datasets built on academic foundations — structured, rights-cleared, and ready for AI applications.
Japanese is not just another language to localize. Its complexity — honorifics, context-dependence, non-verbal integration — makes it a proving ground for AI data quality.
Mastering Japanese elevates standards across all domains.