tombagby
commited on
Commit
·
8e6908c
1
Parent(s):
e6a4a65
Update README.
Browse files
README.md
CHANGED
|
@@ -21,7 +21,23 @@ language:
|
|
| 21 |
- ta
|
| 22 |
- te
|
| 23 |
- ur
|
| 24 |
-
pretty_name: Simple Voice
|
| 25 |
size_categories:
|
| 26 |
- 100K<n<1M
|
| 27 |
-
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 21 |
- ta
|
| 22 |
- te
|
| 23 |
- ur
|
| 24 |
+
pretty_name: Simple Voice Questions
|
| 25 |
size_categories:
|
| 26 |
- 100K<n<1M
|
| 27 |
+
---
|
| 28 |
+
# Simple Voice Questions
|
| 29 |
+
|
| 30 |
+
Simple Voice Questions (SVQ) is a set of short audio questions recorded in 26 locales across 17 languages under multiple audio conditions.
|
| 31 |
+
|
| 32 |
+
## Data Collection
|
| 33 |
+
|
| 34 |
+
Speakers were presented with recording instructions specifying the recording environment and text query to be recorded.
|
| 35 |
+
They recorded using their own phones or tablets under four conditions:
|
| 36 |
+
- clean: Record in quiet environment
|
| 37 |
+
- background speech noise: Record while audio from sources like podcasts, talk radio, or YouTube plays on a separate device (e.g., TV, tablet, computer, or another phone) at a normal listening volume, ensuring it is audible in the recording
|
| 38 |
+
- traffic noise: Record while speaker is a passenger within a moving vehicle. This includes various forms of transport like buses, trains, and cars (where someone else is driving)
|
| 39 |
+
- media noise: Record while background media (music, TV, movies, etc.) is playing on a separate device (TV, tablet, computer, or phone). The playback volume should be a normal listening level,sufficient to be audible in the recording.
|
| 40 |
+
|
| 41 |
+
In all conditions, speakers were instructed to minimize other background noise (like fans or conversations), hold their phone naturally, avoid extra sounds (like clicks or taps), use wired headphones if applicable, and speak naturally and expressively with emotion.
|
| 42 |
+
|
| 43 |
+
The query’s text comes from validation and test sets of the XTREME-UP’s retrieval and question answering benchamark datasets [23]. The XTREME-UP dataset is a collection of TYDI QAdatasets [24] which are question answering datasets covering 11 typologically diverse languages and the professional translation of the cross-lingual open-retrieval question answering (XOR QA) dataset[25] into 23 Indic languages.
|