updates for technical review:

* added updated to multi-modal use
* update to learning objectives and overview pages to specify platforms supported

Author: NinaARM

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

@@ -39,7 +39,7 @@ The voice assistant pipeline imports and builds a separate module to provide thi
 https://gitlab.arm.com/kleidi/kleidi-examples/speech-to-text
 ```
 
-and build for various platforms:
+and build for various platforms to independently benchmark STT functionality:
 
 |Platform|Details|
 |---|---|
@@ -67,7 +67,7 @@ The voice assistant pipeline imports and builds a separate module to provide thi
 https://gitlab.arm.com/kleidi/kleidi-examples/large-language-models
 ```
 
-and build for various platforms:
+and build for various platforms to independently benchmark LLM functionality:
 
 |Platform|Details|
 |---|---|
@@ -94,4 +94,4 @@ This part of the application pipeline uses the Android Text-to-Speech API along
 
 In synchronous mode, speech playback begins only after the full LLM response is received. By default, the application operates in asynchronous mode, where speech synthesis starts as soon as a full or partial sentence is ready. Remaining tokens are buffered and processed by the Android Text-to-Speech engine to ensure uninterrupted playback.
 
-You are now familiar with the building blocks of this application, so you can build the voice assistant for an Android device in the next step.
+You are now familiar with the building blocks of this application and can build these independently for various platforms. You can now build the multi-modal Voice Assistant example which runs on Android OS in the next step.

content/learning-paths/mobile-graphics-and-gaming/voice-assistant/5-kleidiai.md

@@ -31,5 +31,5 @@ To disable KleidiAI during build:
 
 KleidiAI simplifies development by abstracting away low-level optimization: developers can write high-level code while the KleidiAI library selects the most efficient implementation at runtime based on the target hardware. This is possible thanks to its deeply optimized micro-kernels tailored for Arm architectures.
 
-As newer versions of the architecture become available, KleidiAI becomes even more powerful: simply updating the library allows applications like the Voice Assistant to take advantage of the latest architectural improvements - such as SME2 — without requiring any code changes. This means better performance on newer devices with no additional effort from developers.
+As newer versions of the architecture become available, KleidiAI becomes even more powerful: simply updating the library allows applications like the multi-modal Voice Assistant to take advantage of the latest architectural improvements - such as SME2 — without requiring any code changes. This means better performance on newer devices with no additional effort from developers.
 

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

@@ -1,13 +1,15 @@
 ---
-title: Accelerate Voice Assistant performance with KleidiAI and SME2
+title: Accelerate multi-modal Voice Assistant performance with KleidiAI and SME2
 
 minutes_to_complete: 30
 
-who_is_this_for: This is an introductory topic for developers who want to see a multi-model pipeline of a Voice Assistant application and accelerate the performance on Android devices using KleidiAI and SME2.
+who_is_this_for: This is an introductory topic for developers who want to see a pipeline of a multi-modal Voice Assistant application and accelerate the performance on Android devices using KleidiAI and SME2.
 
 learning_objectives:
-    - Compile and run a Voice Assistant Android application.
-    - Optimize performance using KleidiAI and SME2.
+    - Learn about the multi-modal Voice Assistant pipeline and different components used.
+    - Learn about the functionality of ML components used and how these can be built and benchmarked on various platforms.
+    - Compile and run a multi-modal Voice Assistant example based on Android OS.
+    - Optimize performance of multi-modal Voice Assistant 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 8 Pro.