Update image links for lowercase asset names

Author: arm-bhanuarya

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

@@ -7,6 +7,6 @@ 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, we will 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")
+![Voice sentiment classification pipeline#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.

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

@@ -13,7 +13,7 @@ In this section, you will build an end-to-end pipeline that:
 
 This forms the foundation of your voice assistant.
 
-![Baseline voice-to-LLM pipeline#center](3_VSApipeline1.png "Baseline voice-to-LLM pipeline")
+![Baseline voice-to-LLM pipeline#center](3_vsapipeline1.png "Baseline voice-to-LLM pipeline")
 
 Before you begin, make sure you have completed the environment setup in the previous section and that your `llama-server` is still running.
 

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

@@ -158,7 +158,7 @@ models/
 
 We 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")
+![Model ONNX conversion and int-8 quantization results#center](4_modelconversionandcompression.png "Model ONNX conversion and int-8 quantization results")
 
 Typical results look like:
 

content/learning-paths/mobile-graphics-and-gaming/voice-sentiment-analysis-with-llm/6_integrate-voice-sentiment-classification-model.md

@@ -7,7 +7,7 @@ In this section, you will integrate the voice sentiment classification model int
 
 End-to-end response time may take a few seconds because the application runs Whisper transcription, ONNX sentiment inference, and local LLM generation for each interaction.
 
-![Integrated voice-to-LLM pipeline#center](6_VSApipeline4.png "Integrated voice-to-LLM pipeline")
+![Integrated voice-to-LLM pipeline#center](6_vsapipeline4.png "Integrated voice-to-LLM pipeline")