DOI: 10.5281/zenodo.14897750
title: Speech Collection of Styles (SPECS)

description: This is a dataset of keywords extracted from conversational-style speech and command-style speech and it is made up of 3 subsets: command keywords (ck), extended command keywords (eck) and conversational speech (cs). This dataset is intended solely for research on keyword recognition and speech style analysis. 

For each of the three datasets, we asked users to record themselves in a quiet environment, reciting given text excerpts with specific keywords inside them and saving the recordings as 16kHz 32-bit PCM WAVE files.

We added an additional folder of background noises, which is a copy from Google's Speech Commands set of background noises. 

-------------------------------------------------------------------------------------------------------------------------------------------------------------

For command keywords (ck), the users were given 10 text examples, each containing 10 repetitions of the following keywords: on, no, up, off, down, stop, go, right, yes and left. They were asked to pronounce the examples as if answering a device, using an intonation similar to command-style speech; for instance, as if the device asked, "Lights up or lights down?" and they answered "Up!" ten times, with as much variation in intonation between the repetitions as they could produce.

ck sentences:

1) On! (10 times) 

2) No! (10 times) 

3) Up! (10 times) 

4) Off! (10 times) 

5) Down! (10 times) 

6) Stop! (10 times) 

7) Go! (10 times) 

8) Right! (10 times) 

9) Yes! (10 times) 

10) Left! (10 times) 

-------------------------------------------------------------------------------------------------------------------------------------------------------------

For conversational speech (cs), users were given 20 more elaborate text examples such as this one: "She put the book on the table. No other books were there. She then looked up to her mum.". They were asked to recite the examples in their normal speaking tone as if they were telling a story to a friend. The recordings for CS contained a total of 10 instances of the same keywords mentioned for CM. 

cs sentences:

1) She put the book on the table. No other books were there. She then looked up to her mum. 

2) On Friday they were meeting. He had no idea what she was up to. 

3) She climbed up the ladder with no hesitation and stood on the roof. 

4) The alarm clock rang, but no lights were on to wake him up. 

5) He insisted on going up the mountain, even though no one followed. 

6) They were driving up the street on a sunny day. There was no traffic. 

7) The power went on, but there was no heat, so she bundled up in a blanket. 

8) She started the car up, but no radio was on to play her favourite music. 

9) The computer powered up, but no alerts came on the screen. 

10) No wonder she woke up tired. She had been on call 3 times this week already. 

11) She put off the task, unable to find the right resources. Down the hall, the phone rang. 

12) He switched off the lights, quietly stepped down the stairs and turned right. 

13) The cat jumped right off the neighbour’s fence and darted down our garden alley. 

14) She climbed down slowly, then jumped off right at the bottom. 

15) He pulled off his hat and looked right down at his shoes. 

16) They set off down the road, aiming to reach their destination right at dawn. 

17) The papers flew off the table and floated down to the floor, right next to his shoes. 

18) She carefully wiped the mud off her shoes and sat down, turning slightly, to her right. 

19) They turned right off the busy highway and drove down the country road. 

20) She got off the bus and walked down the street, heading right past the blue house. 

-------------------------------------------------------------------------------------------------------------------------------------------------------------

For the extended command keywords (eck), we extended the keywords in the ck dataset by additional 100ms to the left and right. A few keywords needed to be manually corrected. Two speakers were removed from the dataset because they pronounced the keywords very quickly, which resulted in too many bad keywords.