Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 language:
-- en
-thumbnail:
+- es
+thumbnail: null
 tags:
 - audio-classification
 - speechbrain
@@ -11,20 +11,24 @@ tags:
 - wav2vec2
 - XLSR
 - CommonAccent
-license: "mit"
+license: mit
 datasets:
 - CommonVoice
 metrics:
 - Accuracy
 widget:
 - example_title: Caribe-Colombia-Cuba
-  src: https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/caribe-cuba-colombia.wav
+  src: >-
+    https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/caribe-cuba-colombia.wav
 - example_title: Andino
-  src: https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/andino.wav
+  src: >-
+    https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/andino.wav
 - example_title: Mexico
-  src: https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/mexico.wav
+  src: >-
+    https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/mexico.wav
 - example_title: Spain
-  src: https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/spain.wav
+  src: >-
+    https://huggingface.co/Jzuluaga/accent-id-commonaccent_xlsr-spanish/resolve/main/data/spain.wav
 ---
 
 
@@ -34,6 +38,8 @@ widget:
 
 # CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
 
+**Spanish Accent Classifier**
+
 
 **Abstract**: 
 Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accented speech errors. We address multilingual accent classification through the ECAPA-TDNN and Wav2Vec 2.0/XLSR architectures which have been proven to perform well on a variety of speech-related downstream tasks. We introduce a simple-to-follow recipe aligned to the SpeechBrain toolkit for accent classification based on Common Voice 7.0 (English) and Common Voice 11.0 (Italian, German, and Spanish). Furthermore, we establish new state-of-the-art for English accent classification with as high as 95% accuracy. We also study the internal categorization of the Wav2Vev 2.0 embeddings through t-SNE, noting that there is a level of clustering based on phonological similarity.