[host]

app.py

@@ -5,7 +5,8 @@ import numpy as np
 
 # Load the leaderboard data
 def load_data():
-    df = pd.read_csv("leaderboard_results.csv")
+    df = pd.read_csv("leaderboard_results.csv", quotechar='"', escapechar='\\', 
+                     skipinitialspace=True, quoting=pd.io.common.QUOTE_MINIMAL)
     return df
 
 # Create a bar chart visualization of the accuracy scores

leaderboard_results.csv

@@ -1,6 +1,6 @@
 Test Acc,Solution,Institution,Region,Paper,Lead Author
 0.75162,Mosaic,Florida International University,US,Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs,Enshi Zhang
 0.75059,Edinburgh,University of Edinburgh,UK,Context and System Fusion in Post-ASR Emotion Recognition with Large Language Models,"Pavel Stepachev and Pinzhen Chen"
-0.64522,SLAM,Academia Sinica,TW,A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models,"Speech, Language and Music Processing (SLAM) Laboratory"
-0.58809,TeamBlack,Columbia University,US,Post-ASR LLM-Based Speech Emotion Recognition: A fight between top LLMs,Sounak Ray
+0.64522,SLAM,Academia Sinica,TW,A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models,"Speech, Language and Music Processing Laboratory"
+0.58809,TeamBlack,Columbia University,US,"Post-ASR LLM-Based Speech Emotion Recognition: A fight between top LLMs",Sounak Ray
 0.5518,GPT-3.5 Turbo,Baseline,Global,"Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition",Official