saymyname.io

You can create a shareable name card on SayMyName by recording your name once — it takes about a minute — and then pasting the link it gives you wherever your name already appears. According to the site, creating a card is free, requires no credit card, and produces a link in the form saymyname.io/yourname that anyone can open to hear you say your own name. This works for anyone who is tired of correcting people: new hires, people with names that get mispronounced, or anyone who introduces themselves to new groups regularly.

What a name card actually is

A name card is a single link tied to your recorded pronunciation. When someone opens it, they hear your voice saying your name, so they don't have to guess or ask a third time. The site describes it as "record your own name once" and "everyone who meets you from then on gets it right."

Two separate things live on SayMyName, and it helps not to confuse them:

• Searching a name — type any name and hear an AI-generated pronunciation. The site says this needs nothing to install and no sign-up.
• Creating your card — record your own name so others hear you, not a synthetic voice.

This guide covers the second one.

Steps to create your card

1. Go to saymyname.io and choose the option to create your card (the site labels it "Create your free card").
2. Record yourself saying your name. The site states this takes about a minute.
3. Save the card. You get a link in the form saymyname.io/yourname.
4. Test the link yourself before sharing it — open it in a private window to confirm the audio plays for someone who isn't logged in.

Expected result: a working link that plays your recorded pronunciation to anyone who opens it.

Where to put the link

The site's own examples show the card pasted into the places where your name already shows up, so the correction happens before anyone has to ask:

The logic is the same in each case: one link, pasted wherever your name appears, teaches one more person each time.

Common snags

• Recording quality matters more than you'd think. A quiet room and a normal speaking pace give a cleaner result than a rushed recording in a noisy space.
• Say your name the way you want it said. If you use a particular stress or tone, record that version — the card is your reference, not a generic one.
• Check the link before you broadcast it. A card that doesn't play is worse than no card, because people assume they got it right.
• One card covers many contexts. The site notes the same card works at work, at graduation, in class, and in your community, so you don't need separate versions.

When this is worth doing

Create a card if your name is frequently mispronounced, if you're joining a new team or class, or if you introduce yourself to new people often. Skip it if your name is rarely an issue and you don't want another link to maintain.

If you only need to hear how a name is pronounced — someone else's, not your own — use the search function instead; the site says that requires no sign-up.

If you're new to Ahrefs and want to run your first SEO audit, the fastest path is: open Site Explorer, enter your target URL, review the Overview for a health snapshot, then dig into Organic Keywords, Top Pages, and Site Audit to find specific problems. From there, build a short prioritized to-do list instead of trying to fix everything at once.

This tutorial walks through that workflow using a realistic starting scenario, explains what the numbers mean, and shows how to turn findings into actions.

Before You Start: Pick a Narrow Scope

A common beginner mistake is auditing an entire large website on day one. The reports become overwhelming, and you can't tell which issues matter.

Instead, choose one of these starting points:

• A single important page (your homepage or a key product/service page)
• A small site (under ~50 pages, e.g., a personal blog or small business site)
• One section of a bigger site (e.g., /blog/)

For this tutorial, assume you're auditing a small business site with about 30 pages. The same steps scale up later.

You'll need an Ahrefs account to follow along. Ahrefs offers paid plans, and pricing and feature limits change over time, so check the current Pricing page for what's included in each tier before committing.

Step 1: Enter Your Target in Site Explorer

Site Explorer is Ahrefs' core tool for analyzing any website or URL.

1. Open Site Explorer from the top navigation.
2. In the search box, paste your domain (e.g., example.com).
3. Choose the Exact URL or Domain mode depending on scope. For a full-site view, use Domain or Prefix; for a single page, use Exact URL.
4. Press Enter.

You'll land on the Overview report. Don't try to absorb everything — focus on four numbers first.

Reading the Overview Snapshot

Important caveat: Ahrefs' traffic and keyword numbers are estimates based on its own data. They won't match Google Search Console or your analytics exactly. Treat them as directional, not absolute.

Step 2: See What You Already Rank For

Go to Organic Keywords in the left sidebar. This shows queries where your site appears in search results.

Sort by Traffic (descending) to see which pages bring the most estimated visitors. Then look for:

• Keywords ranking in positions 4–15 — these are often the easiest wins. A small content or on-page improvement can push them onto page one.
• Keywords with high volume but low position — potential opportunities if the topic is relevant.
• Irrelevant keywords — if you rank for something off-topic, it may signal thin or mismatched content.

Write down 5–10 of the position 4–15 keywords. These become your first optimization targets.

Step 3: Find Your Best and Weakest Pages

Open Top Pages. This ranks your URLs by estimated organic traffic.

Look for two things:

1. Your top performers — understand what topics and formats work. Can you create more content like this?
2. Pages with traffic but poor rankings — these may need on-page fixes (title, headings, internal links).

If a page gets zero traffic and targets a topic you care about, it's a candidate for a rewrite or consolidation.

Step 4: Run a Technical Site Audit

Now move to Site Audit. This crawls your site and flags technical and on-page issues.

1. Click Site Audit → New project.
2. Enter your domain and set crawl settings (default is usually fine for a small site).
3. Start the crawl and wait for it to finish.

Once complete, you'll see a Health Score and a list of issues grouped by category.

Which Issues to Fix First

Not all issues are equal. Prioritize in this order:

Ignore low-impact warnings (like minor meta description length) until the big items are handled.

Step 5: Turn Findings Into a To-Do List

You now have raw data. Convert it into a short, actionable list. Example:

1. Fix 3 broken links found in Site Audit.
2. Rewrite title tags on 5 pages with duplicate titles.
3. Improve 4 pages ranking in positions 6–12 by adding missing subtopics and internal links.
4. Remove or update 2 thin pages with no traffic.

Keep the list to 5–10 items max for your first audit. Finishing a short list beats starting a long one.

Common Beginner Mistakes

• Chasing every red flag. Site Audit flags many minor issues. Fix what affects rankings and users first.
• Trusting estimates as exact numbers. Ahrefs data is modeled, not measured from your analytics.
• Auditing a huge site too early. Start small to learn the interface.
• Ignoring search intent. A page can be technically perfect but still fail if it doesn't match what searchers want.
• Forgetting to re-crawl. After fixes, run Site Audit again to confirm improvements.

Where to Go Next

Once your first audit is done:

• Compare with competitors using Site Explorer's Competing Domains and Content Gap reports.
• Track keyword rankings over time with Rank Tracker.
• Explore backlink opportunities in the Backlinks and Link Intersect reports.
• Set up recurring Site Audit crawls so new issues surface automatically.

Your first audit isn't about perfection — it's about building a repeatable habit: enter a target, read the key reports, pick the highest-impact fixes, and act. Do that once a month and your site's health compounds.

AI audio transcription converts speech into text by combining signal processing with machine learning models trained on huge amounts of paired audio and text. In practice, the pipeline runs through several stages: audio preprocessing, acoustic and language modeling, punctuation and formatting, and—if enabled—speaker diarization and summarization. Accuracy is not a single fixed number; it depends on recording quality, accents, background noise, overlapping speech, vocabulary, and how well the chosen language is supported. This article explains each stage and the practical factors that move accuracy up or down, so you can judge when automated transcription is enough and when human review still matters.

The core pipeline: from sound wave to readable text

1. Audio preprocessing

Before any speech recognition happens, the file is normalized and cleaned up. Typical steps include:

• Resampling to a consistent sample rate (commonly 16 kHz for speech models).
• Channel handling: mono conversion or selecting the dominant channel when stereo tracks differ.
• Noise reduction and gain normalization to bring quiet speakers up and steady loud peaks.
• Voice activity detection (VAD) to find where speech actually occurs and skip silence.

Good preprocessing improves everything downstream. A clean, consistent input gives the model less to compensate for.

2. Speech recognition (acoustic + language modeling)

Modern systems use neural networks—often transformer-based—that map short audio frames to probable words or subword units. Two components work together:

• The acoustic model estimates which sounds were spoken.
• The language model estimates which word sequences are plausible in the target language.

The decoder combines both to produce the most likely transcript. This is why context matters: a model that "knows" a phrase is common will favor it over a phonetically similar but unlikely alternative.

3. Punctuation, casing, and formatting

Raw recognition output is a stream of words. A separate step adds:

• Sentence boundaries and punctuation.
• Capitalization of proper nouns and sentence starts.
• Number, date, and currency formatting.

These are learned from text data, so they follow the conventions of the training material rather than any single style guide.

4. Speaker diarization

Diarization answers "who spoke when." The system extracts voice characteristics (embeddings) from each speech segment, clusters similar segments, and assigns labels like Speaker 1, Speaker 2. It works best when speakers sound distinct and don't talk over each other. Overlapping speech and similar voices are the main failure modes.

5. Summaries and derived outputs

Once a transcript exists, summarization models condense it into key points, action items, or topics. Because summaries are generated from the transcript, any transcription error can propagate into the summary. Speaker labels also let a summary attribute statements to the right person—if diarization was accurate.

What actually affects accuracy

Accuracy varies widely by conditions. The table below summarizes the main factors and their typical effect.

Language coverage and multilingual models

A system advertising "54+ languages" does not mean equal quality in all of them. High-resource languages (English, Spanish, French, German) usually have more training data and better accuracy. Lower-resource languages may show more errors, especially with specialized terms. Multilingual models can handle code-switching—mixing languages in one conversation—but results depend on how much mixed-language data the model saw. If your content is in a less common language, test a sample before committing.

Domain-specific vocabulary

Names, product terms, medical or legal jargon, and acronyms are frequent error sources because they're rare in general training text. Many tools let you supply a custom vocabulary or keyword list to bias the decoder. This is one of the highest-leverage fixes you can apply.

Practical steps to improve your results

1. Record well. Use a close microphone, a quiet room, and a consistent setup. This single step often matters more than any setting.
2. Use one speaker per channel when possible; it makes diarization trivial and more reliable.
3. Add a custom vocabulary for names, brands, and technical terms.
4. Choose the correct language explicitly rather than relying on auto-detection, especially for short clips.
5. Review the transcript against the audio for high-stakes content.
6. Check speaker labels if attribution matters; correct them before generating summaries.

A simple quality-check template

For any important recording, run this quick pass:

• [ ] Does the transcript match the audio in the first two minutes?
• [ ] Are proper nouns and numbers correct?
• [ ] Are speaker labels consistent and correctly assigned?
• [ ] Do punctuation and paragraph breaks aid readability?
• [ ] Does the summary reflect the actual discussion, not just keywords?

When human review is still needed

Automated transcription is fast and increasingly accurate, but certain situations call for a human pass:

• Legal, medical, or financial records where a single word changes meaning.
• Heavily accented or overlapping speech in noisy environments.
• Highly technical content with dense jargon.
• Anything published under your name where errors carry reputational cost.

A common workflow is machine transcription first, then targeted human editing—this captures most of the speed benefit while controlling risk.

Choosing a tool: what to compare

When evaluating transcription software, compare on the dimensions that match your use case:

• Language support for your specific languages, not just the headline count.
• Speaker detection quality if you need attributed transcripts.
• Custom vocabulary support.
• Export formats (SRT, VTT, DOCX, JSON) for your downstream tools.
• Summarization if you want derived outputs.
• Pricing model—check the vendor's current pricing page, since plans and rates change.

Sonix, for example, positions itself around transcription in 54+ languages with AI summaries and speaker detection, and offers a free trial without a credit card. Verify current features and pricing directly on its site, as these details evolve.

Bottom line

AI transcription works by cleaning audio, recognizing speech with acoustic and language models, then adding punctuation, speaker labels, and summaries. Accuracy is driven less by the model alone and more by your recording conditions, language, vocabulary, and whether speakers overlap. Improve the input, supply domain terms, and reserve human review for high-stakes content—and you'll get reliable results from automated transcription in most everyday cases.

SayMyName is for anyone whose name gets mispronounced — and anyone who has to say other people's names out loud. The site describes two free actions: search any name to hear it pronounced, or record your own name card in about a minute. It lists use cases for workplaces, universities, schools, nonprofits, and individuals, and says the same card works across all of them. If you only need to check one name occasionally, the search side is enough; if you want other people to get your name right without being corrected, the card is the part that matters.

The two things you can do

The site frames the tool around a single split:

• Search a name — type any name and hear it said properly. The page states there is nothing to install and nothing to sign up for.
• Create your card — record your own name once, and share one link so everyone who meets you afterward hears it correctly.

Both are described as free. Card creation is stated as free with no credit card and takes about a minute.

Situations where it fits

At work

The page's workplace examples are team introductions and calls:

• A new starter is greeted properly on day one.
• Before a call or intro, you can hear how a colleague says their own name.
• No one has to ask a third time.
• It works for people you have never met.

The card is shown pasted into an email signature, a chat message, and a calendar invite, so the recipient hears the name before the call starts. The site also shows a team view where colleagues' cards sit side by side with phonetic spellings — for example "Amara Okafor — uh MAH ruh oh KAH for" — so you can hear anyone on your team.

At graduation, in class, and in the community

The page lists dedicated paths for universities (graduation), schools (classrooms), and nonprofits (community settings). The claim is that the same card works in all of them, plus "just for you" for individuals. It also states the tool is already in use at charities, startups, universities, and public bodies.

For individuals

If your only goal is to stop repeating yourself, the card is the relevant feature: record once, then paste the link wherever your name already appears — signature, chat, invite.

Choosing between the two

What to check before relying on it

• Language coverage: the example card shows a language label ("Portuguese (Brazil)") marked Beta, so treat non-English coverage as something to verify per name rather than assume.
• Pricing beyond the free actions: the site has a Pricing page and Login/Sign Up links, but the page evidence only supports "free to create, no credit card" for the card and free search. Anything beyond that is not established here.
• Accuracy: the audio is described as AI-powered. For a name you will say in a high-stakes setting, listen and practice rather than assume the first playback is definitive.

If your need is simply "hear this name before I say it," start with search. If your need is "stop being misnamed," record the card and put the link where people already see your name.

Yes. SayMyName is free for both of its core actions: searching a name to hear it pronounced, and creating your own name card. The site states that creating a card is "Free to create. No credit card. Takes about a minute." Searching a name requires nothing to install and no sign-up. A separate Pricing page exists, so paid options may be offered beyond the free features, but the free tier covers the main use cases described on the homepage.

What you can do for free

Searching and practicing a name

1. Enter a name in the search field.
2. Play the AI-generated audio to hear the pronunciation.
3. Use the practice prompt: press play, then say the name yourself and compare.

The homepage example shows a name card for "João Vitor Martins" with a language label (Portuguese (Brazil)) and a "Practice now" control. No account is needed for this lookup flow.

Creating and sharing your own card

1. Record your name once — the site says this takes about a minute.
2. Get a single link in the form saymyname.io/yourname.
3. Paste that link wherever your name already appears: email signature, chat message, calendar invite, or on a call.

The card displays your name, a phonetic spelling, and a play control so others can hear it. The site's examples include "Amara Okafor — uh MAH ruh oh KAH for" and "Priya Raghunathan — PREE yuh rah goo NAH tun."

Where the free card is meant to be used

The same card is designed to work across contexts, according to the site:

• At work — for workplaces and teams
• At graduation — for universities
• In class — for schools
• In your community — for nonprofits
• Just you — for individuals

The site notes the tool is "already in use at charities, startups, universities and public bodies."

What to check if you need more than the free tier

A Pricing page is linked in the site navigation, which suggests there are paid plans or additional features beyond the free search and card creation. The homepage does not list prices, payment methods, or what a paid tier includes. If your needs go beyond a single personal card or basic name lookups — for example, managing cards for a whole team — check the Pricing page directly for current terms.