Fill out datasheet some more.

Please feel free to consider this preliminary and suggest changes.

README.md

@@ -113,39 +113,46 @@ See our [paper](https://arxiv.org/abs/2111.09344).
 
 #### Initial Data Collection and Normalization
 
-See our [paper](https://arxiv.org/abs/2111.09344).
+Data was downloaded via the archive.org API. No data inference was done.
 
 #### Who are the source language producers?
 
-See our [paper](https://arxiv.org/abs/2111.09344).
+[Needs More Information]
 
 ### Annotations
 
 #### Annotation process
 
-See our [paper](https://arxiv.org/abs/2111.09344).
+No manual annotation is done. We download only source audio with already existing transcripts.
 
 #### Who are the annotators?
 
-See our [paper](https://arxiv.org/abs/2111.09344).
+For the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems. 
 
 ### Personal and Sensitive Information
 
-See our [paper](https://arxiv.org/abs/2111.09344).
+Several of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-See our [paper](https://arxiv.org/abs/2111.09344).
+The dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis.
+
+The dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset.
+
+Our sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time.
+
 
 ### Discussion of Biases
 
-See our [paper](https://arxiv.org/abs/2111.09344).
+Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there.
+
+Almost all of our data is American accented English.
 
 ### Other Known Limitations
 
-[Needs More Information]
+As of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it.
 
 ## Additional Information
 
@@ -159,4 +166,30 @@ We provide CC-BY and CC-BY-SA subsets of the dataset.
 
 ### Citation Information
 
-[Needs More Information]
+Please cite:
+
+```
+@article{DBLP:journals/corr/abs-2111-09344,
+  author    = {Daniel Galvez and
+               Greg Diamos and
+               Juan Ciro and
+               Juan Felipe Cer{\'{o}}n and
+               Keith Achorn and
+               Anjali Gopi and
+               David Kanter and
+               Maximilian Lam and
+               Mark Mazumder and
+               Vijay Janapa Reddi},
+  title     = {The People's Speech: {A} Large-Scale Diverse English Speech Recognition
+               Dataset for Commercial Usage},
+  journal   = {CoRR},
+  volume    = {abs/2111.09344},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2111.09344},
+  eprinttype = {arXiv},
+  eprint    = {2111.09344},
+  timestamp = {Mon, 22 Nov 2021 16:44:07 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```