![]() ![]() Prebuilt neural voicesĮach prebuilt neural voice supports a specific language and dialect, identified by locale. Use the following table to determine supported styles and roles for each neural voice. To learn how you can configure and adjust neural voice styles and roles, see Speech Synthesis Markup Language. ![]() With roles, the same voice can act as a different age and gender. You can optimize the voice for different scenarios like customer service, newscast, and voice assistant. In some cases, you can adjust the speaking style to express different emotions like cheerfulness, empathy, and calm. Please see the table footnotes for more details.Īdditional remarks for Text-to-speech locales are included in the Voice styles and roles, Prebuilt neural voices, and Custom Neural Voice sections below.Ĭheck the the Voice Gallery and determine the right voice for your business needs. The tables in this section summarizes the locales and voices supported for Text-to-speech. ![]() To learn more about customization, see Custom Speech. By default, plain text customization is supported for all available base models. Depending on the locale, you can upload audio + human-labeled transcripts, plain text, structured text, and pronunciation data. To improve Speech-to-text recognition accuracy, customization is available for some languages and base models. Try out the Real-time Speech-to-text tool without having to use any code. Please see the table footnotes for more details.Īdditional remarks for Speech-to-text locales are included in the Custom Speech section below. The table in this section summarizes the locales and voices supported for Speech-to-text. See Speech Containers and Embedded Speech separately for their supported languages. Language support varies by Speech service functionality. You can also get a list of locales and voices supported for each specific region or endpoint through the Speech SDK, Speech-to-text REST API, Speech-to-text REST API for short audio and Text-to-speech REST API. The following tables summarize language support for speech-to-text, text-to-speech, pronunciation assessment, speech translation, speaker recognition, and additional service features. This mode will cause the speech configuration instance to interpret word descriptions of sentence structures such as punctuation.Language and voice support for the Speech service Use the Speech SDK to enable dictation mode when you're using speech-to-text with continuous recognition. The options are apple forward slash banana forward slash orange period This is especially useful in a situation where you want to use complex punctuation without having to merge it later. Then you can speak punctuation aloud in order to make your text more legible. When you're using speech-to-text with continuous recognition, you can configure the Speech service to recognize explicit punctuation marks. Punctuation is helpful for reading back call or conversation transcriptions. Speech-to-text automatically punctuates your text to improve clarity. I uh said that we can go to the uhmm movies Disfluency removal is great for transcribing live unscripted speeches to read them back later. Speech-to-text can recognize such disfluencies and remove them from the display text. ![]() When speaking, it's common for someone to stutter, duplicate words, and say filler words like "uhm" or "uh". For example, the Speech service will automatically capitalize proper nouns and words at the beginning of a sentence. Send it to models recognize words that should be capitalized to improve readability, accuracy, and grammar. My phone number is one eight hundred, four five six, eight nine ten The following table shows the ITN rules that are applied to the text output. You can speak naturally, and the service formats text as expected. Some of the supported text formats include dates, times, decimals, currencies, addresses, emails, and phone numbers. This process is performed by the speech-to-text service and isn't configurable. For example, the spoken word "four" is converted to the written form "4". Inverse Text Normalization (ITN) is a process that converts spoken words into their written form. Below is an overview of these features and how each one is used to improve the overall clarity of the final text output. Speech-to-text offers an array of formatting features to ensure that the transcribed text is clear and legible. ![]()
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