> For the complete documentation index, see [llms.txt](https://twbvoiceplaybook.clearglobal.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://twbvoiceplaybook.clearglobal.org/4.-guidelines-for-sentence-and-prompt-collection/4.1-where-can-i-find-sentences-for-my-language-domain.md). # 4.1 Where can I find sentences for my language/domain? ### Understanding domains and content sources What do we mean by "domain[^1]" when collecting voice data? **Domain** means the subject area or context in which the language is used. For example, healthcare, farming, and legal services are all different domains. They each have their own terminology and language patterns. If you focus on the relevant domain, you can be sure that: 1. the resulting voice model[^2] will be able to handle terminology from your use case and will be accurate, 2. the vocabulary used reflects how people actually speak in real-life situations, 3. the models will be well suited to the intended use, so their impact will be stronger Collecting data in a domain-specific way is very important for languages that are not widely spoken as resources for these languages may be limited. ### Digital versus non-digital sources
### Repurposing existing content One of the most efficient methods is to repurpose content that you've already developed or have access to: * **Previous projects:** past publications, translations, or localization projects often contain sentences in your [target language](#user-content-fn-3)[^3] that have been checked for quality * **Communications materials:** materials created for community outreach, for education, or for information campaigns * **Content generated by users:** questions, answers, and feedback that you have collected through your services (check permissions) ### Online sources There are various sources of sentences you could use on the internet. Availability and permissions to use them vary a lot by language and domain:
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{% endcolumn %} {% endcolumns %} If you use online sources, always check the licensing terms and say where the content came from. ### Community-based collection For languages with limited written resources, it is very helpful to work directly with [community members](#user-content-fn-4)[^4] or linguists: * **Community workshops:** Bring together speakers to generate sentences about their daily lives and local topics * **Storytelling sessions:** Record and transcribe traditional stories (remember that transcription means extra work). You can then process these recordings to increase the amount of voice data you have. * **Surveys and interviews:** Ask community members about specific topics that are relevant to your domain. * **Expert consultation:** Work with linguists or experts in your domain to generate terminology and phrases that relate to your domain. Remember that generating new content means extra work. But you may need to do this if your language has only limited resources. ### Domain-specific resources If your domain is specialized, there are some targeted resources you can use:
### Make sure your collection is well-balanced Try to get a wide variety in your sentence collection:: * **Varying length:** include short, medium, and longer sentences. Keep in mind limitations on recording time. Aim at sentences of 15-20 seconds maximum. * **Sentence types:** statements, questions, exclamations, and commands * **Grammar structure:** different tenses, types of sentence, and levels of complexity * **Range of different speakers:** content from different age groups, genders, and backgrounds * **Variety of situations:** formal and informal language, different social settings, sounds that people make (like “Oh!” and “Aha” in English) When organizing your collection, use a clear system: * Create spreadsheets with separate columns for original sentences and normalized versions ([explained later](https://d.docs.live.net/d189fa3c37514a9a/Documents/01%20Freelance%20paperwork/23%20TWB%20Plain%20Language/Tasks/03%2005%2006%202025%20Dona/PL%20Edit_TWB%20Voice%20Program%20Playbook_Part%202_Sentence%20and%20prompt%20collection%20-%20Copy.docx#_ohcpwwmkgca4)) and notes, where necessary * Make a note of the sources and authors of your sentences so you can be sure to credit them * Track metadata[^5] like domain categories, sentence length, and completion status * Set clear targets for how many sentences you need in each category | :bulb: **Key tips: How many sentences do I need?** | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |

The ideal number of sentences depends on the size, resources, and goals of your project. For reference:

If you have access to plenty of sources of digital text data, collect as many sentences as you can. If you are collecting texts manually, there will be practical limitations so you will need to plan carefully.

Ideally, each sentence should be recorded once by a single speaker. But if you only have a limited collection of sentences, you can get multiple speakers to record the same sentences. This approach is helpful when you are having problems creating a wide variety of content but you still need a lot of voice data—made possible by having a large number of contributors.

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[^1]: **Domain:** The subject area or setting in which we use language. Examples are healthcare, farming, or education. Each domain has its own special terms and language patterns. [^2]: **Model:** A computer-based system that has made use of data to learn patterns. It can make predictions or generate language. In speech technology, models use voice data to learn to recognize speech (converting audio to text) or to create speech (converting text to audio). [^3]: **Target language:** The language(s) for which you are collecting the voice data. [^4]: **Community members:** People at the center of a project who contribute by recording and validating data. In this context, community refers to their shared interest in contributing to data collection and the languages they speak. Community members are well positioned to provide feedback on linguistic aspects and offer input on the project's direction and needs. [^5]: **Metadata:** Extra information that gives some background to a dataset or parts of a dataset. Examples are demographics of the speakers (age, gender, accent), recording conditions (microphone type, level of background noise), or language-related details (dialect, speed of speaking, emotional tone). [^6]: **Dataset:** A collection of information that has been organized for use. A voice dataset is a collection of voice recordings and includes extra information. It is for use in research and for training or improving voice models.