updates after team review

Author: NinaARM

content/learning-paths/mobile-graphics-and-gaming/voice-assistant/2-overview.md

@@ -61,7 +61,7 @@ The text transcription from the previous part of the pipeline is used as input t
 
 By default, the LLM runs asynchronously, streaming tokens as they are generated. The UI updates in real time with each token, which is also passed to the final pipeline stage.
 
-The voice assistant pipeline imports and builds a separate module to provide this STT functionality. You can access this at:
+The voice assistant pipeline imports and builds a separate module to provide this LLM functionality. You can access this at:
 
 ```
 https://gitlab.arm.com/kleidi/kleidi-examples/large-language-models

content/learning-paths/mobile-graphics-and-gaming/voice-assistant/4-run.md

@@ -48,7 +48,7 @@ If you have built the application using the default `llama.cpp` backend, you can
 
 For this, click the image button first:
 
-![use image alt-text#center](voice_assistant_use_multimodal_1.png "Add image button")
+![use image alt-text#center](voice_assistant_multimodal_1.png "Add image button")
 
 This will bring up the photos you can chose from:
 
@@ -60,7 +60,7 @@ Choose the image, and add image for voice assistant:
 
 You can now ask questions related to this image, the large language model will you the image and text for multimodal question answering.
 
-![ask question image alt-text#center](voice_assistant_use_multimodal_2.png "Add image to the question")
+![ask question image alt-text#center](voice_assistant_multimodal_2.png "Add image to the question")
 
 Now that you have explored how the android application is set up and built, you can see in detail how KleidiAI library is used in the next step.
 

content/learning-paths/mobile-graphics-and-gaming/voice-assistant/_index.md

@@ -10,7 +10,7 @@ learning_objectives:
     - Optimize performance using KleidiAI and SME2.
 
 prerequisites:
-    - An Android phone that supports the i8mm Arm architecture feature (8-bit integer matrix multiplication). This Learning Path was tested on a Google Pixel 9 Pro.
+    - An Android phone that supports the i8mm Arm architecture feature (8-bit integer matrix multiplication). This Learning Path was tested on a Google Pixel 8 Pro.
     - A development machine with [Android Studio](https://developer.android.com/studio) installed.
 
 author:
@@ -26,9 +26,9 @@ tools_software_languages:
     - Kotlin
     - C++
 operatingsystems:
+    - Android
     - Linux
     - macOS
-    - Android
 
 further_reading: