Merge pull request #2155 from eric-urban/eur/speech-regions

restructure speech regions table

Author: ttorble

articles/ai-services/speech-service/custom-neural-voice-lite.md

@@ -60,7 +60,7 @@ To create a custom neural voice lite project, follow these steps:
 Record at least 20 voice samples (up to 50) with provided scripts online. Voice samples recorded here are used to create a synthetic version of your voice.
 
 > [!NOTE]
-> Custom neural voice training is currently only available in some regions. See footnotes in the [regions](regions.md#speech-service) table for more information.
+> Custom neural voice training is currently only available in some regions. See footnotes in the [regions](regions.md#regions) table for more information.
 
 Here are some tips to help you record your voice samples:
 - Use a good microphone. Increase the clarity of your samples by using a high-quality microphone. Speak about 8 inches away from the microphone to avoid mouth noises.

articles/ai-services/speech-service/custom-speech-overview.md

@@ -29,7 +29,7 @@ With custom speech, you can upload your own data, test and train a custom model,
 
 Here's more information about the sequence of steps shown in the previous diagram:
 
-1. [Create a project](how-to-custom-speech-create-project.md) and choose a model. Use a <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesSpeechServices" title="Create a Speech resource" target="_blank">Speech resource</a> that you create in the Azure portal. If you train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. For more information, see footnotes in the [regions](regions.md#speech-service) table.
+1. [Create a project](how-to-custom-speech-create-project.md) and choose a model. Use a <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesSpeechServices" title="Create a Speech resource" target="_blank">Speech resource</a> that you create in the Azure portal. If you train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. For more information, see footnotes in the [regions](regions.md#regions) table.
 1. [Upload test data](./how-to-custom-speech-upload-data.md). Upload test data to evaluate the speech to text offering for your applications, tools, and products.
 1. [Test recognition quality](how-to-custom-speech-inspect-data.md). Use the [Speech Studio](https://aka.ms/speechstudio/customspeech) to play back uploaded audio and inspect the speech recognition quality of your test data. 
 1. [Test model quantitatively](how-to-custom-speech-evaluate-data.md). Evaluate and improve the accuracy of the speech to text model. The Speech service provides a quantitative word error rate (WER), which you can use to determine if more training is required. 

articles/ai-services/speech-service/high-definition-voices.md

@@ -39,7 +39,7 @@ Here's a comparison of features between Azure AI Speech HD voices, Azure OpenAI
 
 | Feature | Azure AI Speech HD voices  | Azure OpenAI HD voices | Azure AI Speech voices (not HD) |
 |---------|---------------|------------------------|------------------------|
-| **Region** | East US, Southeast Asia, West Europe | North Central US, Sweden Central | Available in dozens of regions. See the [region list](regions.md#speech-service).|
+| **Region** | East US, Southeast Asia, West Europe | North Central US, Sweden Central | Available in dozens of regions. See the [region list](regions.md#regions).|
 | **Number of voices** | 12 | 6 | More than 500 |
 | **Multilingual**  | No (perform on primary language only) | Yes  | Yes (applicable only to multilingual voices)  |
 | **SSML support** | Support for [a subset of SSML elements](#supported-and-unsupported-ssml-elements-for-azure-ai-speech-hd-voices).|  Support for [a subset of SSML elements](openai-voices.md#ssml-elements-supported-by-openai-text-to-speech-voices-in-azure-ai-speech).  | Support for the [full set of SSML](speech-synthesis-markup-structure.md) in Azure AI Speech.  |

articles/ai-services/speech-service/how-to-audio-content-creation.md

@@ -44,7 +44,7 @@ After you sign up for the Azure account, you need to create a Speech resource in
 It takes a few moments to deploy your new Speech resource. After the deployment is complete, you can start using the Audio Content Creation tool.
 
  > [!NOTE]
-   > If you plan to use neural voices, make sure that you create your resource in [a region that supports neural voices](regions.md#speech-service).
+   > If you plan to use neural voices, make sure that you create your resource in [a region that supports neural voices](regions.md#regions).
 
 ### Step 3: Sign in to Audio Content Creation with your Azure account and Speech resource
 

articles/ai-services/speech-service/how-to-custom-speech-create-project.md

@@ -26,7 +26,7 @@ To create a custom speech project, follow these steps:
 1. Select the subscription and Speech resource to work with. 
 
     > [!IMPORTANT]
-    > If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. See footnotes in the [regions](regions.md#speech-service) table for more information.
+    > If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. See footnotes in the [regions](regions.md#regions) table for more information.
 
 1. Select **Custom speech** > **Create a new project**. 
 1. Follow the instructions provided by the wizard to create your project. 

articles/ai-services/speech-service/how-to-custom-speech-test-and-train.md

@@ -44,7 +44,7 @@ Training with plain text or structured text usually finishes within a few minute
 > 
 > Start with small sets of sample data that match the language, acoustics, and hardware where your model will be used. Small datasets of representative data can expose problems before you invest in gathering larger datasets for training. For sample custom speech data, see <a href="https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/sampledata/customspeech" target="_target">this GitHub repository</a>.
 
-If you train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. For more information, see footnotes in the [regions](regions.md#speech-service) table. In regions with dedicated hardware for custom speech training, the Speech service uses up to 100 hours of your audio training data, and can process about 10 hours of data per day. After the model is trained, you can copy the model to another region as needed with the [Models_CopyTo](/rest/api/speechtotext/models/copy-to) REST API.
+If you train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. For more information, see footnotes in the [regions](regions.md#regions) table. In regions with dedicated hardware for custom speech training, the Speech service uses up to 100 hours of your audio training data, and can process about 10 hours of data per day. After the model is trained, you can copy the model to another region as needed with the [Models_CopyTo](/rest/api/speechtotext/models/copy-to) REST API.
 
 ## Consider datasets by scenario
 
@@ -75,7 +75,7 @@ You can use audio + human-labeled transcript data for both [training](how-to-cus
 For a list of base models that support training with audio data, see [Language support](language-support.md?tabs=stt). Even if a base model does support training with audio data, the service might use only part of the audio. And it still uses all the transcripts.
 
 > [!IMPORTANT]
-> If a base model doesn't support customization with audio data, only the transcription text will be used for training. If you switch to a base model that supports customization with audio data, the training time may increase from several hours to several days. The change in training time would be most noticeable when you switch to a base model in a [region](regions.md#speech-service) without dedicated hardware for training. If the audio data is not required, you should remove it to decrease the training time. 
+> If a base model doesn't support customization with audio data, only the transcription text will be used for training. If you switch to a base model that supports customization with audio data, the training time may increase from several hours to several days. The change in training time would be most noticeable when you switch to a base model in a [region](regions.md#regions) without dedicated hardware for training. If the audio data is not required, you should remove it to decrease the training time. 
 
 Audio with human-labeled transcripts offers the greatest accuracy improvements if the audio comes from the target use case. Samples must cover the full scope of speech. For example, a call center for a retail store would get the most calls about swimwear and sunglasses during summer months. Ensure that your sample includes the full scope of speech that you want to detect.
 

articles/ai-services/speech-service/how-to-custom-speech-train-model.md

@@ -26,7 +26,7 @@ You can use a custom model for a limited time after it was trained. You must per
 > [!IMPORTANT]
 > If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. After a model is trained, you can [copy it to a Speech resource](#copy-a-model) in another region as needed. 
 > 
-> In regions with dedicated hardware for custom speech training, the Speech service will use up to 100 hours of your audio training data, and can process about 10 hours of data per day. See footnotes in the [regions](regions.md#speech-service) table for more information.  
+> In regions with dedicated hardware for custom speech training, the Speech service will use up to 100 hours of your audio training data, and can process about 10 hours of data per day. See footnotes in the [regions](regions.md#regions) table for more information.  
 
 ## Create a model
 
@@ -221,7 +221,7 @@ The top-level `self` property in the response body is the model's URI. Use this
 
 ## Copy a model
 
-You can copy a model to another project that uses the same locale. For example, after a model is trained with audio data in a [region](regions.md#speech-service) with dedicated hardware for training, you can copy it to a Speech resource in another region as needed. 
+You can copy a model to another project that uses the same locale. For example, after a model is trained with audio data in a [region](regions.md#regions) with dedicated hardware for training, you can copy it to a Speech resource in another region as needed. 
 
 ::: zone pivot="speech-studio"
 

articles/ai-services/speech-service/how-to-pronunciation-assessment.md

@@ -30,7 +30,7 @@ In this article, you learn how to evaluate pronunciation with speech to text thr
 
 Pronunciation assessment supports uninterrupted streaming mode. The recording time can be unlimited through the Speech SDK. As long as you don't stop recording, the evaluation process doesn't finish and you can pause and resume evaluation conveniently.
 
-For information about availability of pronunciation assessment, see [supported languages](language-support.md?tabs=pronunciation-assessment) and [available regions](regions.md#speech-service).
+For information about availability of pronunciation assessment, see [supported languages](language-support.md?tabs=pronunciation-assessment) and [available regions](regions.md#regions).
 
 As a baseline, usage of pronunciation assessment costs the same as speech to text for pay-as-you-go or commitment tier [pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services). If you [purchase a commitment tier](../commitment-tier.md) for speech to text, the spend for pronunciation assessment goes towards meeting the commitment. For more information, see [Pricing](./pronunciation-assessment-tool.md#pricing).
 

articles/ai-services/speech-service/includes/how-to/professional-voice/create-project/speech-studio.md

@@ -5,16 +5,15 @@ author: eric-urban
 ms.author: eur
 ms.service: azure-ai-speech
 ms.topic: include
-ms.date: 12/1/2023
-ms.custom: include
+ms.date: 1/6/2025
 ---
 
 Content for [Custom neural voice](https://aka.ms/customvoice) like data, models, tests, and endpoints are organized into projects in Speech Studio. Each project is specific to a country/region and language, and the gender of the voice you want to create. For example, you might create a project for a female voice for your call center's chat bots that use English in the United States.
 
 > [!TIP]
 > Try [Custom neural voice (CNV) Lite](../../../../custom-neural-voice-lite.md) to demo and evaluate CNV before investing in professional recordings to create a higher-quality voice. 
 
-All it takes to get started are a handful of audio files and the associated transcriptions. See if custom neural voice supports your [language](../../../../language-support.md?tabs=tts) and [region](../../../../regions.md#speech-service).
+All it takes to get started are a handful of audio files and the associated transcriptions. See if custom neural voice supports your [language](../../../../language-support.md?tabs=tts) and [region](../../../../regions.md#regions).
 
 ## Create a custom neural voice Pro project
 
@@ -24,13 +23,13 @@ To create a custom neural voice Pro project, follow these steps:
 1. Select the subscription and Speech resource to work with. 
 
     > [!IMPORTANT]
-    > Custom neural voice training is currently only available in some regions. After your voice model is trained in a supported region, you can copy it to a Speech resource in another region as needed. See footnotes in the [regions](../../../../regions.md#speech-service) table for more information.
+    > Custom neural voice training is currently only available in some regions. After your voice model is trained in a supported region, you can copy it to a Speech resource in another region as needed. See footnotes in the [regions](../../../../regions.md#regions) table for more information.
 
 1. Select **Custom voice** > **Create a project**. 
 1. Select **Custom neural voice Pro** > **Next**. 
 1. Follow the instructions provided by the wizard to create your project. 
 
-Select the new project by name or select **Go to project**. You'll see these menu items in the left panel: **Set up voice talent**, **Prepare training data**, **Train model**, and **Deploy model**. 
+Select the new project by name or select **Go to project**. You see these menu items in the left panel: **Set up voice talent**, **Prepare training data**, **Train model**, and **Deploy model**. 
 
 ## Next steps
 

articles/ai-services/speech-service/includes/how-to/professional-voice/deploy-endpoint/speech-studio.md

@@ -25,8 +25,8 @@ To create a custom neural voice endpoint:
 1. Select a voice model that you want to associate with this endpoint.  
 1. Enter a **Name** and **Description** for your custom endpoint.
 1. Select **Endpoint type** according to your scenario. If your resource is in a supported region, the default setting for the endpoint type is *High performance*. Otherwise, if the resource is in an unsupported region, the only available option is *Fast resume*.
-   - *High performance*: Optimized for scenarios with real-time and high-volume synthesis requests, such as conversational AI, call-center bots. It takes around 5 minutes to deploy or resume an endpoint. For information about regions where the *High performance* endpoint type is supported, see the footnotes in the [regions](../../../../regions.md#speech-service) table. 
-   - *Fast resume*: Optimized for audio content creation scenarios with less frequent synthesis requests. Easy and quick to deploy or resume an endpoint in under a minute. The *Fast resume* endpoint type is supported in all [regions](../../../../regions.md#speech-service) where text to speech is available.
+   - *High performance*: Optimized for scenarios with real-time and high-volume synthesis requests, such as conversational AI, call-center bots. It takes around 5 minutes to deploy or resume an endpoint. For information about regions where the *High performance* endpoint type is supported, see the footnotes in the [regions](../../../../regions.md#regions) table. 
+   - *Fast resume*: Optimized for audio content creation scenarios with less frequent synthesis requests. Easy and quick to deploy or resume an endpoint in under a minute. The *Fast resume* endpoint type is supported in all [regions](../../../../regions.md#regions) where text to speech is available.
 
 1. Select **Deploy** to create your endpoint.