Input data and information sources
The asylum seeker provides the speech sample and controls how they speak. The sample is linked to a specific person and their asylum procedure.
Language and dialect recognition in asylum procedures
Also known as: Language and dialect identification in asylum procedures
From a short speech sample, the system estimates which language and dialect an asylum seeker is likely to speak.
Understand the use case
What defines this use case?
It returns probabilities for languages and dialects as an indication of whether the claimed origin is consistent, rather than as proof. Germany's Federal Office for Migration and Refugees calls the procedure dialect recognition software (DIAS) and classifies it as speech biometrics.
This page describes a typical system design. Actual data flows and permissions depend on your implementation.
- System design
- Analysis and processing
- Ability to act
- Action permissions depend on integration
- Fields of use
- migration administration
- Also known as
- speech biometrics, DIAS
How it works, step by step
System design and data flows
Input data
The asylum seeker provides a short speech sample, which is recorded.
The asylum seeker provides the speech sample and controls how they speak. The sample is linked to a specific person and their asylum procedure.
Processing
The model compares pronunciation and sound with the languages and dialects it has learned.
The model analyses linguistic or acoustic characteristics of a recording and assigns them to a language or dialect group. This assignment depends on the trained category system and available comparison data. Linguistic classification alone establishes neither nationality nor an individual's origin.
Results
The model returns probabilities for recognised languages and dialects.
The result describes the defined model task. A match, category or forecast acquires its professional significance only in conjunction with the underlying data and intended use.
Use and impact
The result enters the file as an indication and may influence the interview and the assessment of the claimed origin.
Integration determines whether the result is simply displayed, adopted after approval or written directly into a business application. These variants differ in their data flows and permissions.
Explore the system design: Select a component to see its task, incoming and outgoing data, and role in the use case. The guided walkthrough explains each phase. Then use Yes, Uncertain and No to represent your specific system variant.
6 review first10 clarify7 baseline threats
- Present
- Depends on your system
- Absent
- Review first
- To clarify
How to use the model
The diagram shows the typical architecture. Select a component to see what happens there and adapt its features to your system. Enable “Analysis guidance” to see which threats need reviewing for this architecture. The page's checklist continues to describe the typical case.
Changes from the typical case
None yet. Each change will appear here.
Where most threats arise
- 4 review first
- 4 clarify
- 1 review first, 2 clarify
- 2 clarify
In this system design: 6 to review first, 10 to clarify, 7 baseline threats.
Architecture as a list
External access: Depends on your system
To clarify
Input: Part of every AI system
Review first
Third-party content: Present
AI model: Part of every AI system
Baseline for every AI system
Provider service: Depends on your system
To clarify
Training and feedback: Present
Review first
- Targeted Poisoning / Label Poisoning Review priority
To clarify
Result: Part of every AI system
Baseline for every AI system
Business application or equipment: Depends on your system
To clarify
person: Part of every AI system
Review first
- Overreliance Review priority
Affected person: Present
Unapproved AI services: Depends on your system
To clarify
Supply chain and technical foundations: 5 baseline threats applying to every AI system
Baseline for every AI system
How it differs from similar use cases
Speech recognition and transcription
Transcribes speech without estimating the speaker's origin
Data and information
Processing and operating environment
The model processes the inputs. Whether it runs internally or with a service provider must be established for the specific implementation.
Storage and further use
Logging, retention periods and possible use for training must be established for the specific implementation.
Results and data sharing
Depending on the procedure, the result enters the file directly or only after staff review.
Using a provider does not automatically determine where processing takes place. Storage location, subcontractors, deletion periods and use for training must be checked for the specific deployment.
Assess risks and determine measures
From understanding the system to analysing risks
- Define the system boundary
Record data, participants, interfaces and permissions. Resolve uncertain characteristics in the model.
- Investigate threats
Relate review candidates to actual data flows: what could happen, and who would be affected?
- Assess risk
Assess impacts and likelihood in the deployment context. The number of review candidates is not a risk score.
- Provide evidence of measures
Select appropriate controls, assign responsibilities and define effectiveness checks. Document residual risks and approvals.
Key threats in the application context
- 3 particularly important
- 3 typical
- 10 depending on the system
- 7 additional threats for every AI system
A total of 23 threats from the threat catalogue may be relevant to this use case.
Also review: whether the system disadvantages groups
Check whether selection, ranking or assessment disadvantages people with protected characteristics, such as sex, age or origin.
Priority review points
A dialect probability looks like a measurement but says nothing about where someone grew up. Treating it as proof can make someone whose speech reflects several regions seem untrustworthy.
Targeted Poisoning / Label Poisoning
The model adopts human assignments of speech samples to dialects. Mislabelled or poorly represented dialects lead it to misclassify everyone who speaks them.
The person controls how they speak in the sample. Imitating another dialect or allowing background noise can steer the output.
3 further review candidates in the typical design
Other relevant threats
Targeted queries can recover some personal or protected training data from the model.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Sensitive Information Disclosure
Confidential content can appear in answers, supplied context or logs.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Exfiltration from ML Application
Protected data can leak through tools, network connections, storage or insiders.
Listed because of this characteristic
- Processes confidential data
Up to 10 further threats may apply, depending on your system design. Nine threats need to be reviewed for every AI system. Two of these are particularly important here and are therefore listed above.
System features and open questions
Each Yes adds further threats to your checklist, up to 10 in total. If you cannot yet answer a question, include its threats in your review.
- Added by this Yes
- Also requires a Yes to another question
How your system is built
- Learns from your own data2 threats, plus 1 only in combination with other characteristics
- Changes data or triggers processes1 threat
- Uses another provider's model1 threat
- Uses operational feedback1 threat
How you operate the system
- Accepts queries directly through an interface4 threats
- Accessible over a network2 threats, plus 1 only in combination with other characteristics
- Incurs costs with each query1 threat
- Shares computing resources with other customers or applications1 threat
- Used alongside unapproved AI services1 threat
How your system is built
Can the system change things itself, such as storing data, sending messages, initiating transactions or granting permissions?
Depending on the procedure, the result enters the file directly or only after staff review.
If yes, add
Does your organisation train or fine-tune the model itself using its own data?
If yes, add
Does the model or its base model come from another provider, even if you host it?
If yes, add
Does operational feedback or output later feed into analysis or training?
If yes, add
How you operate the system
These questions arise for almost every AI system.
Can people or programs query the model directly, for example through an interface?
If yes, add
Is the system accessible over a network or dependent on network services?
If yes, add
Do you pay per query or unit of computing time, so costs increase with use?
If yes, add
Does the system share servers or computing resources with other customers or applications?
If yes, add
Do staff also use AI services for this task that your organisation has not approved?
If yes, add
Controls and implementation
Measures for the priority review areas are expanded. Each linked measure explains how to implement it.
Review priorityOverreliance3 measures
Review priorityTargeted Poisoning / Label Poisoning3 measures
Review priorityAdversarial Inputs2 measures
Privacy Attacks3 measures
Sensitive Information Disclosure3 measures
Exfiltration from ML Application3 measures
Controls for the remaining threats are available on their pages in the threat catalogue.
AI Act provisions
-
Art. 4: AI literacy
For every AI system Applies since 2 February 2025.
-
Annex III no. 7: Migration, asylum and border control
In typical use Review trigger from 2 December 2027.
Applies to every AI system
Art. 4: AI literacy
Applies if staff or appointed representatives operate or use AI systems.
Applies since 2 February 2025.
Obligations and details for Art. 4: AI literacy
- Who bears the obligations
- Provider, Deployer, Product manufacturer, Provider of a general-purpose AI model
Detailed obligations
May apply in typical use
Annex III no. 7: Migration, asylum and border control
Applies if an authority uses the result to examine an asylum application, for example to check the claimed origin.
Applies from 2 December 2027.
Obligations and details for Annex III no. 7: Migration, asylum and border control
- The general condition is
- Applies if the system is used in examining asylum, visa or residence applications or at border controls.
- Who bears the obligations
- Provider, Deployer, Product manufacturer
Detailed obligations
Classify your system with the quick checker All obligations in the obligations compass
Cross-cutting threats
Review these threats for every AI system, whatever its purpose.
Atypical system features
View 18 further questions
If any of these features does apply to your system, add the listed threats.
Does the system generate its own text, such as answers, drafts or summaries?
The system produces language and dialect probabilities, rather than text.
If yes, add
Does the system generate or edit images, sounds, voices or videos?
If yes, add
Does the system create new content from open-ended requests that people read, see or hear directly? Translation, reading aloud and transcription do not count.
If yes, add
Can users give the system natural language tasks that it then performs?
If yes, add
Does the system search its own collection of documents or records for relevant content?
If yes, add
Does the system formulate answers from retrieved documents?
If yes, add
Does the system use a knowledge graph storing concepts and their relationships?
If yes, add
Does the system remember content from earlier conversations or tasks and reuse it later?
If yes, add
Does the system call other programs, interfaces or search services itself, even if only to read?
If yes, add
Does the system determine its next steps itself based on earlier results?
If yes, add
Does the system delegate tasks to other AI agents or incorporate their results?
If yes, add
Is the system connected to tools or data sources through the Model Context Protocol (MCP)?
If yes, add
Do documents from several customers, departments or permission groups share one search store?
If yes, add
Can people outside your organisation use the system, including without login?
Only office staff operate the system.
If yes, add
Does the system process several input types together, such as text with images or speech?
If yes, add
Does the system generate code, scripts or database queries for further use?
If yes, add
Does the system execute programs, scripts or commands itself?
If yes, add
Is the system intended to maximise a specified metric, such as revenue, utilisation or clicks?
If yes, add
Sources
- Authority, court or parliament
German Bundestag, hib 887/2023
The German government reports the use of dialect recognition at the Federal Office for Migration and Refugees.
- Other source
AlgorithmWatch, Controversial dialect recognition at BAMF
AlgorithmWatch confirms the office's terms dialect recognition software (DIAS) and speech biometrics and reports the European pilot project.
- Legislation, norm or standard
The Act expressly lists AI systems used in examining asylum and visa applications in Annex III.
Related use cases
Also in the field of Administration, justice and public safety
- Risk-based review selection for applications and benefitsAuthorities assess applications or payments for error or fraud risk and use this to select cases for closer review.
- Automated property valuation (AVM)A model estimates residential property market values from property characteristics and comparable sales.
- Police data analysis platformsPolice analysis platforms combine data from different sources and display links between people, incidents and cases as graphs, tables or maps.
- AI assistance in high-volume court proceedingsIn high-volume proceedings, the system organises pleadings, extracts each case's features and prepares draft decisions.
- Automatic number plate recognition (ANPR)Cameras capture vehicle number plates, and the system automatically checks them against registration or police search data.
- Intelligent video analytics in public spacesThe system detects specific events or objects in camera images and alerts a control centre without identifying people.
Often used together with
Updated: 30 September 2026. Review checklist rules version 0.3.2, threat catalogue version 2026.10.24.2. How these pages are structured