Update README.md

README.md

@@ -33,9 +33,10 @@ datasets:
 
 ## Model description
 
-Matcha-TTS is an encoder-decoder architecture designed for fast acoustic modelling in TTS. The encoder predicts phoneme durations and its mean feature vectors 
-modelling alignment with Monotonic Alignment Search (MOS). And the decoder is essentially a U-Net inspired by Grad-TTS, that is based on Transformers architecture combined 
-with 1D instead of 2D CNNs, making a high reduction on memory consumption and speedy synthesis. 
+Matcha-TTS is an encoder-decoder architecture designed for fast acoustic modelling in TTS. The encoder predicts phoneme durations and its mean feature vectors. 
+And the decoder is essentially a U-Net inspired by Grad-TTS, that is based on Transformers architecture combined 
+with 1D instead of 2D CNNs, making a high reduction on memory consumption and speedy synthesis.
+
 Matcha-TTS is non-autorregressive and is trained using optimal-transport conditional flow matching (OT-CFM). 
 This yields an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching.
 
@@ -99,6 +100,20 @@ Data comes from two different datasets: festcat and openslr69
 
 ### Results
 
+## Citation
+
+If this code contributes to your research, please cite the work:
+
+```
+@misc{mehta2024matchatts,
+      title={Matcha-TTS: A fast TTS architecture with conditional flow matching}, 
+      author={Shivam Mehta and Ruibo Tu and Jonas Beskow and Éva Székely and Gustav Eje Henter},
+      year={2024},
+      eprint={2309.03199},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS}
+}
+```
 
 ## Additional information