In SpokenSQuAD, the document is in spoken form, the input question is in the form of text and the answer to each question is always a span in the document. The following procedures were used to generate spoken documents from the original SQuAD dataset. First, the Google text-to-speech system was used to generate the spoken version of the articles in SQuAD. Then CMU Sphinx was sued to generate the corresponding ASR transcriptions. The SQuAD training set was used to generate the training set of Spoken SQuAD, and SQuAD development set was used to generate the testing set for Spoken SQuAD. If the answer of a question did not exist in the ASR transcriptions of the associated article, the question-answer pair was removed from the dataset because these examples are too difficult for listening comprehension machine at this stage.
Source: https://github.com/chiahsuan156/Spoken-SQuAD
Image Source: https://github.com/chiahsuan156/Spoken-SQuAD
Variants: Spoken-SQuAD
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Spoken Language Understanding | ALBERT | End-to-end Spoken Conversational Question Answering: … | 2022-04-29 |
| Spoken Language Understanding | SpeechBERT | SpeechBERT: An Audio-and-text Jointly Learned … | 2019-10-25 |
| Spoken Language Understanding | QANet + GAN | Mitigating the Impact of Speech … | 2019-04-16 |
| Spoken Language Understanding | Baseline | Spoken SQuAD: A Study of … | 2018-04-01 |
Recent papers with results on this dataset: