Update README.md

README.md

@@ -391,11 +391,11 @@ Data collection methods vary across individual datasets. For example, the above
 
 
 ### Diarization Error Rate (DER)
-* All evaluations include overlapping speech.  
-* Collar tolerance is 0s for DIHARD III Eval, and 0.25s for CALLHOME-part2 and CH109.
-* Post-Processing (PP) is optimized on two different held-out dataset splits. 
+* All evaluations include overlapping speech.   
+* Collar tolerance is 0s for DIHARD III Eval, and 0.25s for CALLHOME-part2 and CH109.  
+* Post-Processing (PP) is optimized on two different held-out dataset splits.  
     - [DIHARD III Dev Optimized Post-Processing](https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_tasks/diarization/conf/post_processing/diar_streaming_sortformer_4spk-v2_dihard3-dev.yaml) for DIHARD III Eval    
-    - [CALLHOME-part1 Optimized Post-Processing](https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_tasks/diarization/conf/post_processing/diar_streaming_sortformer_4spk-v2_callhome-part1.yaml) for CALLHOME-part2 and CH109
+    - [CALLHOME-part1 Optimized Post-Processing](https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_tasks/diarization/conf/post_processing/diar_streaming_sortformer_4spk-v2_callhome-part1.yaml) for CALLHOME-part2 and CH109  
     - 
 | **Latency** | *PP* | **DIHARD III Eval <=4spk** | **DIHARD III Eval >=5spk** | **DIHARD III Eval full** | **CALLHOME-part2 2spk** | **CALLHOME-part2 3spk** | **CALLHOME-part2 4spk** | **CALLHOME-part2 5spk** | **CALLHOME-part2 6spk** | **CALLHOME-part2 full** | **CH109** |
 |-------------|------|----------------------------|----------------------------|--------------------------|-------------------------|-------------------------|-------------------------|-------------------------|-------------------------|-------------------------|-----------|
@@ -421,13 +421,19 @@ Also check out the [Riva live demo](https://developer.nvidia.com/riva#demos).
 
 ## References
 
-[1] [Sortformer: Seamless Integration of Speaker Diarization and ASR by Bridging Timestamps and Tokens](https://arxiv.org/abs/2409.06656)
-[2] [Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering](https://arxiv.org/abs/2507.18446)
-[3] [NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech Processing Tasks](https://arxiv.org/abs/2408.13106)
-[4] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)
-[5] [Attention is all you need](https://arxiv.org/abs/1706.03762)
-[6] [NVIDIA NeMo Framework](https://github.com/NVIDIA/NeMo)
-[7] [NeMo speech data simulator](https://arxiv.org/abs/2310.12371)
+[1] [Sortformer: Seamless Integration of Speaker Diarization and ASR by Bridging Timestamps and Tokens](https://arxiv.org/abs/2409.06656)  
+
+[2] [Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering](https://arxiv.org/abs/2507.18446)  
+
+[3] [NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech Processing Tasks](https://arxiv.org/abs/2408.13106)  
+
+[4] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)  
+
+[5] [Attention is all you need](https://arxiv.org/abs/1706.03762)  
+
+[6] [NVIDIA NeMo Framework](https://github.com/NVIDIA/NeMo)  
+
+[7] [NeMo speech data simulator](https://arxiv.org/abs/2310.12371)  
 
 ## Licence