Why pronunciation problems happen<\/h2>\n\n\n
A text-to-speech system has to infer how written language should sound. That is straightforward when spelling, grammar, and context point to one likely reading. It becomes harder when the written form is ambiguous.<\/p>\n\n\n
For example, \u201cJordan\u201d<\/strong> could be a person\u2019s name or a country. \u201cUS\u201d<\/strong> could be read as the letters \u201cU-S\u201d or as the word \u201cus.\u201d \u201c2026\u201d<\/strong> may need to sound like \u201ctwenty twenty-six,\u201d \u201ctwo thousand twenty-six,\u201d or \u201ctwo zero two six,\u201d depending on the context. A model can make a reasonable guess and still choose the reading you did not intend.<\/p>\n\n\n Pronunciation issues commonly come from:<\/p>\n\n\n The goal is not to change the meaning of the script. It is to remove ambiguity so the voice has a clearer path to the intended spoken form.<\/p>\n\n\n Before generating a long voiceover, read the script as a listener would. Mark every item that could have more than one spoken interpretation. This is especially important for opening lines, calls to action, legal copy, proper nouns, and numbers that carry business or instructional meaning.<\/p>\n\n\n Create a small pronunciation list with four columns:<\/p>\n\n\n This list becomes useful when a script changes or when multiple people are producing voiceovers from the same terminology. It also prevents an editor from fixing one instance of a name while missing another later in the script.<\/p>\n\n\n Names are often the highest-risk part of a voiceover because spelling is not a dependable pronunciation guide. A name may be unfamiliar, have multiple accepted pronunciations, or be pronounced differently by the individual who uses it.<\/p>\n\n\n When possible, use a first-party source: the person, organization, client, brand guide, event organizer, or official company recording. Do not assume that a familiar-looking spelling has a familiar pronunciation.<\/p>\n\n\n If a stakeholder gives you a phonetic spelling, save it with the approved script. Ask for a short example sentence as well. Pronunciation can change slightly depending on the words around a name.<\/p>\n\n\n If the platform does not pronounce a name correctly from its standard spelling, test a phonetic rewrite in a duplicate of the sentence. For example, a test version of a name might use syllable breaks or a more speech-friendly spelling. Keep the original spelling in the on-screen copy, captions, or written materials where accuracy matters.<\/p>\n\n\n Avoid overly complicated phonetic spellings. The best rewrite is the smallest change that produces the intended result. A rewrite that helps one voice may not work the same way with another voice, so test it with the selected voice rather than treating it as universal.<\/p>\n\n\n A name can sound correct in isolation but change when followed by punctuation, a possessive ending, or another word. Test these variants when relevant:<\/p>\n\n\n Not every all-caps term should be spoken the same way. Some abbreviations are initialisms, such as \u201cFBI,\u201d and are usually pronounced letter by letter. Others are acronyms, such as \u201cNASA,\u201d and are commonly pronounced as words. Some terms vary by industry or audience.<\/p>\n\n\n Write the intended delivery down before editing the script. Then test the acronym in its actual sentence.<\/p>\n\n\n Use punctuation or spacing only when it improves the result in your selected voice. For example, testing \u201cQ.B.R.\u201d and \u201cQ B R\u201d may produce different pacing or emphasis. Listen for whether the letters remain distinct and whether the voice inserts unwanted pauses.<\/p>\n\n\n Be careful with terms that can become ordinary words. \u201cUS,\u201d for instance, may be interpreted differently depending on capitalization and sentence context. If the intended reading is \u201cU-S,\u201d test it in a sentence rather than assuming capitalization will be enough.<\/p>\n\n\n If an acronym is meant to sound like a word, the surrounding sentence should reinforce that reading. Avoid punctuation that forces a letter-by-letter interpretation. If the term has a less obvious pronunciation, test a phonetic script version and keep an approved pronunciation note for future projects.<\/p>\n\n\n A useful editorial rule is to prioritize listener comprehension over typographic purity in the voice script. The written version for publication can preserve the official styling, while the voiceover script can use a pronunciation-friendly form where needed.<\/p>\n\n\n Numbers have more possible readings than many teams expect. A string of digits might be a year, a quantity, a street address, an account number, a phone number, a version, or a date. The correct spoken form depends on what the number represents.<\/p>\n\n\n Consider these common distinctions:<\/p>\n\n\n For critical numbers, write out the desired spoken form in the voiceover script. This is particularly useful for instructions, financial information, addresses, dates, measurements, and promotional offers. A listener cannot look back at a number they misheard.<\/p>\n\n\n Symbols are efficient in visual copy but can introduce uncertainty in speech. A slash may mean \u201cor,\u201d \u201cper,\u201d or a date separator. A hyphen may signal a range, a negative value, or part of a product code. An ampersand may need to be spoken as \u201cand.\u201d<\/p>\n\n\n Where the script must be exact, replace symbols with the words you want the audience to hear. Then test the surrounding sentence for natural pacing.<\/p>\n\n\n Speech Synthesis Markup Language (SSML) provides standardized elements for controlling aspects of synthesized speech, including interpretation of text and pronunciation-related information. The W3C Speech Synthesis Markup Language specification<\/a> defines elements such as `say-as`, `phoneme`, and `sub` for these purposes.<\/p>\n\n\n However, support can vary by text-to-speech platform, voice, editor, and export workflow. Do not assume that every SSML feature will be available or behave identically in every environment. If your workflow supports markup, use the smallest amount necessary and preview the result. If it does not, a carefully prepared spoken-script rewrite is often the clearer option.<\/p>\n\n\n A practical approach is:<\/p>\n\n\n 1. Start with normal written text.<\/p>\n\n\n 2. Test a short clip containing the problematic term.<\/p>\n\n\n 3. Try a script rewrite that makes the intended reading clear.<\/p>\n\n\n 4. Use supported markup only when it improves the output and fits the production workflow.<\/p>\n\n\n 5. Save the approved version in your pronunciation list.<\/p>\n\n\nStart with a pronunciation audit<\/h2>\n\n\n
![\"Start](\"https:\/\/typecast.ai\/learn\/wp-content\/uploads\/2026\/10\/26q4_blog11_a-v2.webp\")
<\/figure>\n\n\nScript item<\/th> Intended spoken form<\/th> Why it may be unclear<\/th> Test sentence<\/th><\/tr><\/thead> AcmeQ<\/td> \u201cACK-me cue\u201d<\/td> Mixed capitalization<\/td> \u201cAcmeQ is available today.\u201d<\/td><\/tr> QBR<\/td> \u201ccue bee ar\u201d<\/td> Acronym<\/td> \u201cOur QBR begins Monday.\u201d<\/td><\/tr> 401(k)<\/td> \u201cfour oh one kay\u201d<\/td> Number and parenthetical letter<\/td> \u201cUpdate your 401(k) contribution.\u201d<\/td><\/tr> 3.5%<\/td> \u201cthree point five percent\u201d<\/td> Decimal and symbol<\/td> \u201cGrowth reached 3.5%.\u201d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n Fix names by writing for the ear<\/h2>\n\n\n
Confirm the preferred pronunciation first<\/h3>\n\n\n
Use a readable phonetic rewrite for testing<\/h3>\n\n\n
Test the whole sentence, not just the name<\/h3>\n\n\n
Decide whether an acronym should be letters or a word<\/h2>\n\n\n
![\"Decide](\"https:\/\/typecast.ai\/learn\/wp-content\/uploads\/2026\/10\/26q4_blog11_b-v2.webp\")
<\/figure>\n\n\nFor letter-by-letter readings<\/h3>\n\n\n
For acronym-as-word readings<\/h3>\n\n\n
Make numbers explicit<\/h2>\n\n\n
Write numbers according to their role<\/h3>\n\n\n
Avoid ambiguous symbols when clarity matters<\/h3>\n\n\n
Use markup only when it is supported and appropriate<\/h2>\n\n\n
![\"Use](\"https:\/\/typecast.ai\/learn\/wp-content\/uploads\/2026\/10\/26q4_blog11_c-v2.webp\")
<\/figure>\n\n\nBuild a repeatable Typecast review workflow<\/h2>\n\n\n
![\"Final](\"https:\/\/typecast.ai\/learn\/wp-content\/uploads\/2026\/10\/26q4_blog11_d-v2.webp\")
<\/figure>\n\n\n