Enhance clarity in voice sentiment analysis learning path documentation

Author: madeline-underwood

content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/1_overview.md

@@ -10,6 +10,6 @@ Voice-based LLM applications often rely primarily on transcribed text from speec
 
 To address this, voice-based sentiment classification analyzes audio input to determine the user's emotional state, which is then incorporated into the LLM prompt to enable more context-aware responses. In this Learning Path, you'll build a sentiment-aware voice assistant that runs entirely on-device. The application records audio, performs transcription—converting speech into written text—using Whisper, classifies sentiment directly from the voice signal, and combines the transcript and voice-based sentiment to guide responses from a local LLM running with llama.cpp.
 
-![Voice sentiment classification pipeline#center](1_vsapipeline2.png "Voice sentiment classification pipeline")
+![Pipeline diagram showing audio input flowing through Whisper transcription and HuBERT sentiment classification, then both text and sentiment being combined into an LLM prompt that generates context-aware responses#center](1_vsapipeline2.png "Voice sentiment classification pipeline")
 
-You will start by building a baseline voice-to-LLM pipeline—capturing audio, transcribing it into text, and using it to generate responses with an LLM. You will then extend this pipeline with a voice-based sentiment classification model. This involves training the model, optimizing it for efficient on-device inference, and integrating it into a unified application.
+You'll start by building a baseline voice-to-LLM pipeline—capturing audio, transcribing it into text, and using it to generate responses with an LLM. You'll then extend this pipeline with a voice-based sentiment classification model. This involves training the model, optimizing it for efficient on-device inference, and integrating it into a unified application.

content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/3_build-baseline-voice-to-llm-pipeline.md

@@ -8,10 +8,10 @@ layout: learningpathall
 
 In this section, you'll build an end-to-end pipeline that:
 
-1. Records audio from your microphone
-2. Transcribes it to text using Whisper
-3. Sends the text to a locally hosted LLM
-4. Displays the model's response
+- Records audio from your microphone
+- Transcribes it to text using Whisper
+- Sends the text to a locally hosted LLM
+- Displays the model's response
 
 This forms the foundation of your voice assistant.
 

content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/5_compress-and-convert-the-model.md

@@ -201,7 +201,7 @@ models/
 
 You can also compare the model metrics before and after quantization.
 
-![Model ONNX conversion and int-8 quantization results#center](4_modelconversionandcompression.png "Model ONNX conversion and int-8 quantization results")
+![Diagram showing model file sizes and performance metrics comparing the original ONNX model to the int-8 quantized version, demonstrating reduced size and improved inference speed#center](4_modelconversionandcompression.png "Model ONNX conversion and int-8 quantization results")
 
 Typical results look like: