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articles/azure-video-indexer/face-detection.md

@@ -37,7 +37,7 @@ This article discusses faces detection and the key considerations for making use
 |Insight  |The information and knowledge derived from the processing and analysis of video and audio files that generate different types of insights and can include detected objects, people, faces, animated characters, keyframes and translations or transcriptions. |
 |Face recognition  |The analysis of images to identify the faces that appear in the images. This process is implemented via the Azure Cognitive Services Face API. |
 |Template |Enrolled images of people are converted to templates, which are then used for facial recognition. Machine-interpretable features are extracted from one or more images of an individual to create that individual’s template. The enrollment or probe images aren't stored by Face API and the original images can't be reconstructed based on a template. Template quality is a key determinant on the accuracy of your results. |
-|Enrolment |The process of enrolling images of individuals for template creation so they can be recognized. When a person is enrolled to a verification system used for authentication, their template is also associated with a primary identifier2 that is used to determine which template to compare with the probe template. High-quality images and images representing natural variations in how a person looks (for instance wearing glasses, not wearing glasses) generate high-quality enrollment templates. |
+|Enrollment |The process of enrolling images of individuals for template creation so they can be recognized. When a person is enrolled to a verification system used for authentication, their template is also associated with a primary identifier2 that is used to determine which template to compare with the probe template. High-quality images and images representing natural variations in how a person looks (for instance wearing glasses, not wearing glasses) generate high-quality enrollment templates. |
 |Deep search  |The ability to retrieve only relevant video and audio files from a video library by searching for specific terms within the extracted insights.|
 
 ## View the insight
@@ -103,14 +103,14 @@ During the Faces Detection procedure, images in a media file are processed, as f
 |---|---|
 |Source file  |	The user uploads the source file for indexing. |
 |Detection and aggregation	|The face detector identifies the faces in each frame. The faces are then aggregated and grouped.   |
-|Recognition	|The celebrities module runs over the aggregated groups to recognize celebrities. If the customer has created their own **persons** module it's also run to recognize people. When people aren't recognized, they're labelled Unknown1, Unknown2 and so on. |
+|Recognition	|The celebrities module runs over the aggregated groups to recognize celebrities. If the customer has created their own **persons** module it's also run to recognize people. When people aren't recognized, they're labeled Unknown1, Unknown2 and so on. |
 |Confidence value	|Where applicable for well-known faces or faces identified in the customizable list, the estimated confidence level of each label is calculated as a range of 0 to 1. The confidence score represents the certainty in the accuracy of the result. For example, an 82% certainty is represented as an 0.82 score.|
 
 ## Example use cases 
 
 * Summarizing where an actor appears in a movie or reusing footage by deep searching for specific faces in organizational archives for insight on a specific celebrity. 
 * Improved efficiency when creating feature stories at a news or sports agency, for example deep searching for a celebrity or football player in organizational archives. 
-* Using faces appearing in the video to create promos, trailers or highlights. Azure Video Indexer can assist by adding keyframes, scene markers, timestamps and labelling so that content editors invest less time reviewing numerous files.   
+* Using faces appearing in the video to create promos, trailers or highlights. Azure Video Indexer can assist by adding keyframes, scene markers, timestamps and labeling so that content editors invest less time reviewing numerous files.   
 
 ## Considerations when choosing a use case 
 

articles/azure-video-indexer/labels-identification.md

@@ -90,7 +90,7 @@ During the Labels procedure, objects in a media file are processed, as follows:
 |Component|Definition|
 |---|---|
 |Source	|The user uploads the source file for indexing. |
-|Tagging|	Images are tagged and labelled. For example, door, chair, woman, headphones, jeans. |
+|Tagging|	Images are tagged and labeled. For example, door, chair, woman, headphones, jeans. |
 |Filtering and aggregation	|Tags are filtered according to their confidence level and aggregated according to their category.|
 |Confidence level|	The estimated confidence level of each label is calculated as a range of 0 to 1. The confidence score represents the certainty in the accuracy of the result. For example, an 82% certainty is represented as an 0.82 score.|
 
@@ -104,7 +104,7 @@ During the Labels procedure, objects in a media file are processed, as follows:
 ## Considerations when choosing a use case 
 
 - Carefully consider the accuracy of the results, to promote more accurate detections, check the quality of the video, low quality video might impact the detected insights. 
-- Carefully consider when using for law enforcement that Labels can potentially not detect parts of the video. To ensure fair and high-quality decisions, combine Labels with human oversight. 
+- Carefully consider when using for law enforcement that Labels potentially cannot detect parts of the video. To ensure fair and high-quality decisions, combine Labels with human oversight. 
 - Don't use labels identification for decisions that may have serious adverse impacts. Machine learning models can result in undetected or incorrect classification output. Decisions based on incorrect output could have serious adverse impacts. Additionally, it's advisable to include human review of decisions that have the potential for serious impacts on individuals. 
 
 When used responsibly and carefully, Azure Video Indexer is a valuable tool for many industries. To respect the privacy and safety of others, and to comply with local and global regulations, we recommend the following:   

articles/azure-video-indexer/topics-inference.md

@@ -90,7 +90,7 @@ During the topics indexing procedure, topics are extracted, as follows:
 |---|---|
 |Source language	|The user uploads the source file for indexing.|
 |Pre-processing|Transcription, OCR and facial recognition AIs extract insights from the media file.|
-|Insights processing|	Topics AI analyze the transcription, OCR and facial recognition insights extracted during pre-processing: <br/>-	Transcribed text, each line of transcribed text insight is examined using ontology-based AI technologies. <br/>-	OCR and Facial Recognition insights are examined together using ontology-based AI technologies.  |
+|Insights processing|	Topics AI analyzes the transcription, OCR and facial recognition insights extracted during pre-processing: <br/>-	Transcribed text, each line of transcribed text insight is examined using ontology-based AI technologies. <br/>-	OCR and Facial Recognition insights are examined together using ontology-based AI technologies.  |
 |Post-processing	|- Transcribed text, insights are extracted and tied to a Topic category together with the line number of the transcribed text. For example, Politics in line 7.<br/>- OCR and Facial Recognition, each insight is tied to a Topic category together with the time of the topic’s instance in the media file. For example, Freddie Mercury in the People and Music categories at 20.00. |
 |Confidence value	|The estimated confidence level of each topic is calculated as a range of 0 to 1. The confidence score represents the certainty in the accuracy of the result. For example, an 82% certainty is represented as an 0.82 score.|
 

articles/azure-video-indexer/transcription-translation-lid.md

@@ -14,7 +14,7 @@ ms.topic: article
 
 Azure Video Indexer transcription, translation and language identification automatically detects, transcribes, and translates the speech in media files into over 50 languages. 
 
-- Azure Video Indexer processes the speech in the audio file to extract the transcription that is then translated into many languages. When selecting to translate into a specific language, both the transcription and the insights like keywords, topics, labels or OCR are translated into the specified language. Transcription can be used as is or be combined with speaker insights that maps and assigns the transcripts into speakers. Multiple speakers can be detected in an audio file. An ID is assigned to each speaker and is displayed under their transcribed speech. 
+- Azure Video Indexer processes the speech in the audio file to extract the transcription that is then translated into many languages. When selecting to translate into a specific language, both the transcription and the insights like keywords, topics, labels or OCR are translated into the specified language. Transcription can be used as is or be combined with speaker insights that map and assign the transcripts into speakers. Multiple speakers can be detected in an audio file. An ID is assigned to each speaker and is displayed under their transcribed speech. 
 -	Azure Video Indexer language identification (LID) automatically recognizes the supported dominant spoken language in the video file. For more information, see [Applying LID](/azure/azure-video-indexer/language-identification-model). 
 -	Azure Video Indexer multi-language identification (MLID) automatically recognizes the spoken languages in different segments in the audio file and sends each segment to be transcribed in the identified languages. At the end of this process, all transcriptions are combined into the same file. For more information, see [Applying MLID](/azure/azure-video-indexer/multi-language-identification-transcription).
 The resulting insights are generated in a categorized list in a JSON file that includes the ID, language, transcribed text, duration and confidence score.