Datasets:

seba3y
/

speechocean762

spk stringclasses 125 values	age stringclasses 25 values	gender stringclasses 2 values	utt_name stringlengths 9 9	utt_text stringlengths 3 61	utt_accuracy int64 3 10	utt_completeness float64 6.7 10	utt_fluency int64 1 10	utt_prosodic int64 1 10	utt_total int64 2 10	words sequencelengths 1 10	words_accuracy sequencelengths 1 10	words_stress sequencelengths 1 10	words_total sequencelengths 1 10	phones sequencelengths 1 10	phones_godness sequencelengths 1 10	duration float64 1.59 20.4
0001	6	m	000010011	WE CALL IT BEAR	8	10	9	9	8	[ "WE", "CALL", "IT", "BEAR" ]	[ 10, 10, 10, 6 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 6 ]	[ [ "W", "IY0" ], [ "K", "AO0", "L" ], [ "IH0", "T" ], [ "B", "EH0", "R" ] ]	[ [ 2, 2 ], [ 2, 1.8, 1.8 ], [ 2, 2 ], [ 2, 1, 1 ] ]	2.58
0001	6	m	000010035	ZERO THREE FIVE ONE	8	10	9	9	8	[ "ZERO", "THREE", "FIVE", "ONE" ]	[ 8, 8, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 7, 8, 10, 10 ]	[ [ "Z", "IH1", "R", "OW0" ], [ "TH", "R", "IY0" ], [ "F", "AY0", "V" ], [ "W", "AH0", "N" ] ]	[ [ 2, 1.4, 1.4, 2 ], [ 1.2, 2, 2 ], [ 2, 2, 1.8 ], [ 2, 2, 2 ] ]	3.43
0001	6	m	000010053	THREE TWO TWO SEVEN	9	10	10	10	9	[ "THREE", "TWO", "TWO", "SEVEN" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "TH", "R", "IY0" ], [ "T", "UW0" ], [ "T", "UW0" ], [ "S", "EH1", "V", "N" ] ]	[ [ 1.8, 2, 2 ], [ 2, 1.8 ], [ 2, 1.8 ], [ 2, 2, 2, 2 ] ]	3.35
0001	6	m	000010063	ELEPHANTS TAI GOOSE	10	10	9	9	9	[ "ELEPHANTS", "TAI", "GOOSE" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "EH1", "L", "IH0", "F", "AH0", "N", "T", "S" ], [ "T", "AY0" ], [ "G", "UW0", "S" ] ]	[ [ 2, 2, 2, 2, 1.8, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	3.319
0001	6	m	000010069	TOM GIVES UP BOXING	9	10	9	10	9	[ "TOM", "GIVES", "UP", "BOXING" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "T", "AA0", "M" ], [ "G", "IH0", "V", "Z" ], [ "AH0", "P" ], [ "B", "AA1", "K", "S", "IH0", "NG" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 1.8 ], [ 2, 2 ], [ 2, 2, 2, 2, 2, 2 ] ]	3.01
0001	6	m	000010075	HE HATES SHOOTING	9	10	9	9	9	[ "HE", "HATES", "SHOOTING" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "HH", "IY0" ], [ "HH", "EY0", "T", "S" ], [ "SH", "UW1", "T", "IH0", "NG" ] ]	[ [ 2, 1.8 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2, 2 ] ]	2.59
0001	6	m	000010089	MANDY HAS A BIG ARM	10	10	9	9	9	[ "MANDY", "HAS", "A", "BIG", "ARM" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "M", "AE1", "N", "D", "IY0" ], [ "HH", "AE0", "Z" ], [ "AH0" ], [ "B", "IH0", "G" ], [ "AA0", "R", "M" ] ]	[ [ 2, 2, 2, 2, 2 ], [ 2, 2, 2 ], [ 2 ], [ 2, 2, 2 ], [ 2, 1.6, 2 ] ]	3.08
0001	6	m	000010095	LOOK AT ANN'S PANTS	10	10	9	9	9	[ "LOOK", "AT", "ANN'S", "PANTS" ]	[ 10, 10, 8, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 8, 10 ]	[ [ "L", "UH0", "K" ], [ "AE0", "T" ], [ "AE0", "N", "Z" ], [ "P", "AE0", "N", "T", "S" ] ]	[ [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 1.4 ], [ 2, 2, 2, 1.8, 1.8 ] ]	2.97
0001	6	m	000010106	WHAT ABOUT THE BUS	9	10	9	8	8	[ "WHAT", "ABOUT", "THE", "BUS" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "W", "AH0", "T" ], [ "AH0", "B", "AW1", "T" ], [ "DH", "AH0" ], [ "B", "AH0", "S" ] ]	[ [ 2, 1.8, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	2.13
0001	6	m	000010113	THEN HE WENT TO THEME PARK	6	10	9	8	5	[ "THEN", "HE", "WENT", "TO", "THEME", "PARK" ]	[ 10, 8, 10, 10, 2, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 8, 10, 10, 3, 10 ]	[ [ "DH", "EH0", "N" ], [ "HH", "IY0" ], [ "W", "EH0", "N", "T" ], [ "T", "UW0" ], [ "TH", "IY0", "M" ], [ "P", "AA0", "R", "K" ] ]	[ [ 2, 2, 2 ], [ 1, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 1.2, 0, 1.6 ], [ 2, 2, 1.8, 2 ] ]	3.002
0001	6	m	000010115	LET'S GO TO THE RESTROOM	9	10	9	9	9	[ "LET'S", "GO", "TO", "THE", "RESTROOM" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "L", "EH0", "T", "S" ], [ "G", "OW0" ], [ "T", "UW0" ], [ "DH", "AH0" ], [ "R", "EH1", "S", "T", "R", "UW0", "M" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2, 2, 2, 1.8 ] ]	3
0001	6	m	000010121	THEN MIKE WALKS TO COFFEE	9	10	9	9	8	[ "THEN", "MIKE", "WALKS", "TO", "COFFEE" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "DH", "EH0", "N" ], [ "M", "AY0", "K" ], [ "W", "AO0", "K", "S" ], [ "T", "UW0" ], [ "K", "AO1", "F", "IY0" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 1.8, 2, 2 ], [ 2, 2 ], [ 2, 1.2, 1.6, 1.6 ] ]	3.31
0001	6	m	000010122	SO MARY WENT ON TO STUDY	9	10	9	9	9	[ "SO", "MARY", "WENT", "ON", "TO", "STUDY" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "S", "OW0" ], [ "M", "AE1", "R", "IH0" ], [ "W", "EH0", "N", "T" ], [ "AA0", "N" ], [ "T", "UW0" ], [ "S", "T", "AH1", "D", "IY0" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 1.8, 2, 2 ] ]	3.42
0001	6	m	000010133	KATE GOT THE TOMATO	8	10	8	8	8	[ "KATE", "GOT", "THE", "TOMATO" ]	[ 8, 10, 10, 8 ]	[ 10, 10, 10, 10 ]	[ 8, 10, 10, 8 ]	[ [ "K", "EH0", "T" ], [ "G", "AA0", "T" ], [ "DH", "AH0" ], [ "T", "AH0", "M", "EY1", "T", "OW0" ] ]	[ [ 2, 1.6, 1.8 ], [ 2, 1.6, 2 ], [ 2, 2 ], [ 2, 2, 2, 1, 1.8, 2 ] ]	2.88
0001	6	m	000010135	TINA LOVES EGGPLANT	8	10	9	9	8	[ "TINA", "LOVES", "EGGPLANT" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "T", "IY1", "N", "AH0" ], [ "L", "AH0", "V", "Z" ], [ "EH1", "G", "P", "L", "AE0", "N", "T" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2, 2, 1.6 ], [ 1.6, 2, 2, 2, 2, 2, 2 ] ]	3.222
0001	6	m	000010140	DORA IS NOT A CLEANER	8	10	9	9	8	[ "DORA", "IS", "NOT", "A", "CLEANER" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "D", "AO1", "R", "AH0" ], [ "IH0", "Z" ], [ "N", "AA0", "T" ], [ "AH0" ], [ "K", "L", "IY1", "N", "ER0" ] ]	[ [ 2, 1.8, 2, 2 ], [ 2, 2 ], [ 2, 1.6, 2 ], [ 2 ], [ 2, 2, 2, 2, 2 ] ]	3.325
0001	6	m	000010145	MARK LIVED IN NEW YORK	9	10	9	9	9	[ "MARK", "LIVED", "IN", "NEW", "YORK" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "M", "AA0", "R", "K" ], [ "L", "IH0", "V", "D" ], [ "IH0", "N" ], [ "N", "UW0" ], [ "Y", "AO0", "K" ] ]	[ [ 2, 2, 1.8, 1.6 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	3.49
0001	6	m	000010149	BOB LIVES IN CAIRO NOW	8	10	9	8	8	[ "BOB", "LIVES", "IN", "CAIRO", "NOW" ]	[ 10, 10, 10, 8, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 8, 10 ]	[ [ "B", "AA0", "B" ], [ "L", "IH0", "V", "Z" ], [ "IH0", "N" ], [ "K", "AY1", "R", "OW0" ], [ "N", "AW0" ] ]	[ [ 2, 1.4, 2 ], [ 2, 2, 2, 1.6 ], [ 2, 2 ], [ 2, 1.6, 1.6, 1.4 ], [ 2, 2 ] ]	3.28
0001	6	m	000010168	BYE	10	10	10	10	10	[ "BYE" ]	[ 10 ]	[ 10 ]	[ 10 ]	[ [ "B", "AY0" ] ]	[ [ 2, 2 ] ]	1.67
0001	6	m	000010173	TREES	9	10	10	9	9	[ "TREES" ]	[ 8 ]	[ 10 ]	[ 8 ]	[ [ "T", "R", "IY0", "Z" ] ]	[ [ 2, 2, 2, 1.2 ] ]	2.059
0005	6	m	000050003	MIKE LIKES THE WHITE ONE	8	10	6	7	7	[ "MIKE", "LIKES", "THE", "WHITE", "ONE" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "M", "AY0", "K" ], [ "L", "AY0", "K", "S" ], [ "DH", "AH0" ], [ "W", "AY0", "T" ], [ "W", "AH0", "N" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 1.6, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ] ]	4.343
0005	6	m	000050010	ITS NAME IS SAY	7	10	7	7	7	[ "ITS", "NAME", "IS", "SAY" ]	[ 10, 10, 7, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 7, 10 ]	[ [ "IH0", "T", "S" ], [ "N", "EY0", "M" ], [ "IH0", "Z" ], [ "S", "EY0" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2 ], [ 0.8, 1.4 ], [ 2, 1.4 ] ]	3.047
0005	6	m	000050024	BILLY LOVES AMERICA	7	10	6	6	6	[ "BILLY", "LOVES", "AMERICA" ]	[ 10, 8, 10 ]	[ 10, 10, 10 ]	[ 10, 8, 10 ]	[ [ "B", "IH1", "L", "IY0" ], [ "L", "AH0", "V", "Z" ], [ "AH0", "M", "EH1", "R", "IH0", "K", "AH0" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2, 1.8, 1.4 ], [ 2, 2, 2, 2, 2, 1.2, 2 ] ]	3.839
0005	6	m	000050028	TWO TWO EIGHT SEVEN	8	10	8	8	8	[ "TWO", "TWO", "EIGHT", "SEVEN" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "T", "UW0" ], [ "T", "UW0" ], [ "EY0", "T" ], [ "S", "EH1", "V", "N" ] ]	[ [ 2, 1.8 ], [ 2, 1.8 ], [ 2, 2 ], [ 2, 2, 1.8, 2 ] ]	2.723
0005	6	m	000050038	FOUR FIVE FOUR SEVEN	7	10	8	7	7	[ "FOUR", "FIVE", "FOUR", "SEVEN" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "F", "AO0", "R" ], [ "F", "AY0", "V" ], [ "F", "AO0", "R" ], [ "S", "EH1", "V", "N" ] ]	[ [ 2, 2, 1.8 ], [ 2, 2, 1.8 ], [ 2, 2, 1.8 ], [ 2, 1.8, 1.8, 2 ] ]	2.877
0005	6	m	000050040	FIVE THREE NINE ZERO	8	10	8	7	8	[ "FIVE", "THREE", "NINE", "ZERO" ]	[ 10, 8, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 8, 10, 10 ]	[ [ "F", "AY0", "V" ], [ "TH", "R", "IY0" ], [ "N", "AY0", "N" ], [ "Z", "IH", "AH1", "OW0" ] ]	[ [ 2, 2, 1.6 ], [ 1.6, 2, 1.8 ], [ 2, 2, 2 ], [ 2, 1.8, 1.8, 2 ] ]	2.762
0005	6	m	000050047	SIX FIVE THREE	7	10	8	7	7	[ "SIX", "FIVE", "THREE" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "S", "IH0", "K", "S" ], [ "F", "AY0", "V" ], [ "TH", "R", "IY0" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2, 1.8 ], [ 1.4, 2, 2 ] ]	2.254
0005	6	m	000050049	TWO FIVE EIGHT	7	10	9	8	7	[ "TWO", "FIVE", "EIGHT" ]	[ 10, 8, 10 ]	[ 10, 10, 10 ]	[ 10, 8, 10 ]	[ [ "T", "UW0" ], [ "F", "AY0", "V" ], [ "EY0", "T" ] ]	[ [ 2, 2 ], [ 2, 2, 1.4 ], [ 2, 1.8 ] ]	2.129
0005	6	m	000050055	WE LESS MEAT	9	10	8	8	8	[ "WE", "LESS", "MEAT" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "W", "IY0" ], [ "L", "EH0", "S" ], [ "M", "IY0", "T" ] ]	[ [ 2, 2 ], [ 2, 1.6, 2 ], [ 2, 2, 2 ] ]	2.238
0005	6	m	000050078	DOSE MIKE LIKE THE HAMBURGER	7	10	7	6	7	[ "DOSE", "MIKE", "LIKE", "THE", "HAMBURGER" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "D", "OW0", "S" ], [ "M", "AY0", "K" ], [ "L", "AY0", "K" ], [ "DH", "AH0" ], [ "HH", "AE1", "M", "B", "ER0", "G", "ER0" ] ]	[ [ 2, 1.6, 2 ], [ 1.6, 1.4, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2, 2, 2, 2 ] ]	3.87
0005	6	m	000050079	ANNIE WANT HAVE SOME PRETTIES	7	10	6	6	6	[ "ANNIE", "WANT", "HAVE", "SOME", "PRETTIES" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 9, 10, 10, 10 ]	[ [ "AE1", "N", "IH0" ], [ "W", "AA0", "N", "T" ], [ "HH", "AE0", "V" ], [ "S", "AH0", "M" ], [ "P", "R", "IH1", "T", "IY0", "S" ] ]	[ [ 1.8, 2, 2 ], [ 2, 1.6, 1.8, 2 ], [ 2, 2, 1.8 ], [ 2, 2, 2 ], [ 2, 2, 2, 2, 2, 2 ] ]	4.641
0005	6	m	000050083	KATE'S GOT SOME GREY	6	7.5	7	7	5	[ "KATE'S", "GOT", "SOME", "GREY" ]	[ 8, 10, 10, 0 ]	[ 10, 10, 10, 10 ]	[ 8, 10, 10, 2 ]	[ [ "K", "EH0", "IY0", "T", "Z" ], [ "G", "AA0", "T" ], [ "S", "AH0", "M" ], [ "G", "R", "EY0" ] ]	[ [ 2, 2, 2, 1.8, 1.2 ], [ 2, 1.6, 2 ], [ 2, 2, 2 ], [ 0.4, 0.4, 0.4 ] ]	2.435
0005	6	m	000050095	LOOK AT JOHN'S SWEATER	7	10	6	7	7	[ "LOOK", "AT", "JOHN'S", "SWEATER" ]	[ 10, 10, 5, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 6, 10 ]	[ [ "L", "UH0", "K" ], [ "AE0", "T" ], [ "JH", "AA0", "N", "Z" ], [ "S", "W", "EH1", "T", "ER0" ] ]	[ [ 2, 2, 2 ], [ 2, 2 ], [ 2, 1, 1.6, 1.2 ], [ 1.6, 2, 2, 2, 2 ] ]	2.901
0005	6	m	000050099	TIM HAS A BEAUTIFUL TELL	7	10	8	6	7	[ "TIM", "HAS", "A", "BEAUTIFUL", "TELL" ]	[ 10, 10, 10, 10, 3 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 4 ]	[ [ "T", "IH0", "M" ], [ "HH", "AE0", "Z" ], [ "AH0" ], [ "B", "Y", "UW1", "T", "IH0", "F", "L" ], [ "T", "EH0", "L" ] ]	[ [ 2, 1.8, 2 ], [ 2, 2, 1.8 ], [ 2 ], [ 2, 2, 2, 2, 2, 2, 2 ], [ 2, 1.2, 0.8 ] ]	2.85
0005	6	m	000050100	JIM LIKES YOUR BLUE SHORE	8	10	8	7	7	[ "JIM", "LIKES", "YOUR", "BLUE", "SHORE" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "JH", "IH1", "M" ], [ "L", "AY0", "K", "S" ], [ "Y", "ER0" ], [ "B", "L", "UW0" ], [ "SH", "AO0", "R" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 1.6 ], [ 2, 2, 2 ], [ 2, 2, 1.8 ] ]	2.92
0005	6	m	000050114	ANNIE WENT WALKING THEM PARK	8	10	8	6	7	[ "ANNIE", "WENT", "WALKING", "THEM", "PARK" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "AE1", "N", "IH0" ], [ "W", "EH0", "N", "T" ], [ "W", "AO1", "K", "IH0", "NG" ], [ "DH", "EH0", "M" ], [ "P", "AA0", "R", "K" ] ]	[ [ 2, 2, 2 ], [ 2, 1.6, 1.6, 2 ], [ 2, 2, 2, 2, 2 ], [ 2, 1.4, 1.4 ], [ 2, 2, 1.8, 2 ] ]	3.909
0005	6	m	000050118	LAYLA CAN SEE THE HOMETOWN	6	10	7	6	5	[ "LAYLA", "CAN", "SEE", "THE", "HOMETOWN" ]	[ 10, 10, 10, 10, 3 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 4 ]	[ [ "L", "EY1", "L", "AA0" ], [ "K", "AE0", "N" ], [ "S", "IY0" ], [ "DH", "AH0" ], [ "HH", "OW1", "M", "T", "AW0", "N" ] ]	[ [ 1.2, 1.2, 2, 2 ], [ 2, 2, 1.8 ], [ 2, 1.6 ], [ 2, 2 ], [ 2, 2, 2, 2, 0.4, 1.6 ] ]	3.559
0005	6	m	000050122	SO TINA WENT ON TO WASHROOM	6	10	6	5	5	[ "SO", "TINA", "WENT", "ON", "TO", "WASHROOM" ]	[ 10, 10, 2, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 3, 10, 10, 10 ]	[ [ "S", "OW0" ], [ "T", "IY1", "N", "AH0" ], [ "W", "EH0", "N", "T" ], [ "AA0", "N" ], [ "T", "UW0" ], [ "W", "AA1", "SH", "R", "UW0", "M" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 2 ], [ 1.6, 0.4, 0.8, 1.8 ], [ 1.8, 2 ], [ 2, 1.8 ], [ 2, 1.2, 2, 2, 2, 2 ] ]	4.654
0005	6	m	000050174	ALL WITH HIM	8	10	7	6	7	[ "ALL", "WITH", "HIM" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "AO0", "L" ], [ "W", "IH0", "TH" ], [ "HH", "IH0", "M" ] ]	[ [ 1.6, 2 ], [ 2, 2, 1.6 ], [ 1.8, 2, 2 ] ]	1.942
0005	6	m	000050175	GOOD JOB	9	10	9	7	8	[ "GOOD", "JOB" ]	[ 10, 10 ]	[ 10, 10 ]	[ 10, 10 ]	[ [ "G", "UH0", "D" ], [ "JH", "AA0", "B" ] ]	[ [ 2, 1.2, 2 ], [ 2, 1.4, 2 ] ]	1.59
0006	6	f	000060015	JAYME IS GOING TO SEE HEN	9	10	9	9	9	[ "JAYME", "IS", "GOING", "TO", "SEE", "HEN" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "JH", "EY1", "M", "IY0" ], [ "IH0", "Z" ], [ "G", "OW0", "IH0", "NG" ], [ "T", "UW0" ], [ "S", "IY0" ], [ "HH", "EH0", "N" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	3.31
0006	6	f	000060020	DORA IS COME FROM JAPAN	9	10	9	8	8	[ "DORA", "IS", "COME", "FROM", "JAPAN" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "D", "AO1", "R", "AH0" ], [ "IH0", "Z" ], [ "K", "AH0", "M" ], [ "F", "R", "AH0", "M" ], [ "JH", "AH0", "P", "AE1", "N" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 1.4, 2 ], [ 2, 2, 2, 2, 2 ] ]	3.26
0006	6	f	000060029	SIX ONE SIX ZERO	9	10	9	9	9	[ "SIX", "ONE", "SIX", "ZERO" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "S", "IH0", "K", "S" ], [ "W", "AH0", "N" ], [ "S", "IH0", "K", "S" ], [ "Z", "IH1", "R", "OW0" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 1.6, 1.6, 2 ] ]	3.4
0006	6	f	000060031	FIVE TWO FOUR SEVEN	9	10	9	9	9	[ "FIVE", "TWO", "FOUR", "SEVEN" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "F", "AY0", "V" ], [ "T", "UW0" ], [ "F", "AO0", "R" ], [ "S", "EH1", "V", "N" ] ]	[ [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2, 2 ] ]	3.26
0006	6	f	000060049	ZERO THREE FIVE	8	10	9	9	8	[ "ZERO", "THREE", "FIVE" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "Z", "IH1", "R", "OW0" ], [ "TH", "R", "IY0" ], [ "F", "AY0", "V" ] ]	[ [ 2, 1.6, 1.6, 2 ], [ 2, 2, 2 ], [ 2, 2, 1.6 ] ]	3.12
0006	6	f	000060056	PHONE PARTS TICKETS	10	10	9	9	9	[ "PHONE", "PARTS", "TICKETS" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "F", "OW0", "N" ], [ "P", "AA0", "R", "T", "S" ], [ "T", "IH1", "K", "IH0", "T", "S" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2, 2 ], [ 2, 2, 2, 2, 2, 2 ] ]	2.834
0006	6	f	000060077	ANN ATE A LITTLE DOG	9	10	9	9	9	[ "ANN", "ATE", "A", "LITTLE", "DOG" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "AE0", "N" ], [ "EY0", "T" ], [ "AH0" ], [ "L", "IH1", "T", "L" ], [ "D", "AO0", "G" ] ]	[ [ 2, 2 ], [ 2, 2 ], [ 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ] ]	3.05
0006	6	f	000060081	DOES LAYLA LIKE THE JAM	10	10	10	9	9	[ "DOES", "LAYLA", "LIKE", "THE", "JAM" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "D", "AH0", "Z" ], [ "L", "EY1", "L", "AA0" ], [ "L", "AY0", "K" ], [ "DH", "AH0" ], [ "JH", "AE0", "M" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	3.031
0006	6	f	000060082	KATE LIKES LAMB	9	10	9	9	9	[ "KATE", "LIKES", "LAMB" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "K", "EH0", "T" ], [ "L", "AY0", "K", "S" ], [ "L", "AE0", "M" ] ]	[ [ 2, 1.8, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ] ]	3
0006	6	f	000060083	MARK GOT SOME NOODLES	10	10	9	9	9	[ "MARK", "GOT", "SOME", "NOODLES" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "M", "AA0", "R", "K" ], [ "G", "AA0", "T" ], [ "S", "AH0", "M" ], [ "N", "UW1", "D", "L", "Z" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 1.8, 2 ], [ 2, 2, 2 ], [ 2, 2, 2, 2, 1.6 ] ]	2.82
0006	6	f	000060094	TOM LIKES THE OLD SWEATER	10	10	9	9	9	[ "TOM", "LIKES", "THE", "OLD", "SWEATER" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "T", "AA0", "M" ], [ "L", "AY0", "K", "S" ], [ "DH", "AH0" ], [ "OW0", "L", "D" ], [ "S", "W", "EH1", "T", "ER0" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 1.8, 1.6, 1.6 ] ]	3.18
0006	6	f	000060102	BOB NEEDS NEW BOOTS	10	10	10	9	9	[ "BOB", "NEEDS", "NEW", "BOOTS" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "B", "AA0", "B" ], [ "N", "IY0", "D", "Z" ], [ "N", "UW0" ], [ "B", "UW0", "T", "S" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 1.8, 1.8 ], [ 2, 2 ], [ 2, 2, 2, 2 ] ]	3.037
0006	6	f	000060106	TOM CAN SEE THE MOTOR CYCLE	10	10	10	9	9	[ "TOM", "CAN", "SEE", "THE", "MOTOR", "CYCLE" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "T", "AA0", "M" ], [ "K", "AE0", "N" ], [ "S", "IY0" ], [ "DH", "AH0" ], [ "M", "OW1", "T", "ER0" ], [ "S", "AY1", "K", "L" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2 ] ]	3.23
0006	6	f	000060111	HE LEFT THE FRUIT STAND	10	10	9	8	9	[ "HE", "LEFT", "THE", "FRUIT", "STAND" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "HH", "IY0" ], [ "L", "EH0", "F", "T" ], [ "DH", "AH0" ], [ "F", "R", "UW0", "T" ], [ "S", "T", "AE0", "N", "D" ] ]	[ [ 1.6, 1.8 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2, 2 ] ]	3.27
0006	6	f	000060113	LET'S GO TO THE GYM	10	10	9	8	9	[ "LET'S", "GO", "TO", "THE", "GYM" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "L", "EH0", "T", "S" ], [ "G", "OW0" ], [ "T", "UW0" ], [ "DH", "AH0" ], [ "JH", "IH0", "M" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	2.44
0006	6	f	000060116	SO MARK WHEN TO THE PET SHOP	10	10	10	9	9	[ "SO", "MARK", "WHEN", "TO", "THE", "PET", "SHOP" ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ [ "S", "OW0" ], [ "M", "AA0", "R", "K" ], [ "W", "EH0", "N" ], [ "T", "UW0" ], [ "DH", "AH0" ], [ "P", "EH0", "T" ], [ "SH", "AA0", "P" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ] ]	3.08
0006	6	f	000060124	SO ALICE WENT ON TO VILLAGE	9	10	8	9	9	[ "SO", "ALICE", "WENT", "ON", "TO", "VILLAGE" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "S", "OW0" ], [ "AE1", "L", "IH0", "S" ], [ "W", "EH0", "N", "T" ], [ "AA0", "N" ], [ "T", "UW0" ], [ "V", "IH1", "L", "IH0", "JH" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 1.8, 2 ], [ 2, 2 ], [ 2, 2, 2, 2, 2 ] ]	3.579
0006	6	f	000060130	NO THAT'S TOMATO	10	10	9	9	9	[ "NO", "THAT'S", "TOMATO" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "N", "OW0" ], [ "DH", "AE0", "T", "S" ], [ "T", "AH0", "M", "EY1", "T", "OW0" ] ]	[ [ 2, 2 ], [ 1.6, 2, 2, 2 ], [ 2, 2, 2, 2, 2, 2 ] ]	3
0006	6	f	000060136	I'M NOT A ACTOR	9	10	9	8	9	[ "I'M", "NOT", "A", "ACTOR" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "AY0", "M" ], [ "N", "AA0", "T" ], [ "AH0" ], [ "AE1", "K", "T", "ER0" ] ]	[ [ 2, 2 ], [ 2, 1.8, 2 ], [ 2 ], [ 2, 2, 2, 2 ] ]	2.59
0006	6	f	000060153	WHAT LOVELY CLEAN TEETH	9	10	9	8	9	[ "WHAT", "LOVELY", "CLEAN", "TEETH" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "W", "AH0", "T" ], [ "L", "AH1", "V", "L", "IY0" ], [ "K", "L", "IY0", "N" ], [ "T", "IY0", "TH" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 1.8 ] ]	3.12
0026	6	f	000260001	LAYLA LOVE BROWN	6	10	8	8	6	[ "LAYLA", "LOVE", "BROWN" ]	[ 5, 8, 10 ]	[ 10, 10, 10 ]	[ 6, 8, 10 ]	[ [ "L", "EY1", "L", "AA0" ], [ "L", "AH0", "V" ], [ "B", "R", "AW0", "N" ] ]	[ [ 2, 0.4, 2, 2 ], [ 2, 2, 1 ], [ 2, 2, 1.8, 2 ] ]	2.382
0026	6	f	000260011	ANDY CAN SEE THE HEN	8	10	8	9	8	[ "ANDY", "CAN", "SEE", "THE", "HEN" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "AE0", "N", "D", "IH0" ], [ "K", "AE0", "N" ], [ "S", "IY0" ], [ "DH", "AH0" ], [ "HH", "EH0", "N" ] ]	[ [ 1.8, 2, 2, 2 ], [ 2, 1.8, 2 ], [ 2, 2 ], [ 1.8, 2 ], [ 2, 2, 2 ] ]	3.181
0026	6	f	000260015	JOHN IS GOING TO SEE COW	9	10	9	9	9	[ "JOHN", "IS", "GOING", "TO", "SEE", "COW" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "JH", "AH0", "N" ], [ "IH0", "Z" ], [ "G", "OW0", "IH0", "NG" ], [ "T", "UW0" ], [ "S", "IY0" ], [ "K", "AW0" ] ]	[ [ 2, 2, 2 ], [ 2, 1.8 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ] ]	3.22
0026	6	f	000260032	ONE ONE ZERO EIGHT	8	10	9	9	8	[ "ONE", "ONE", "ZERO", "EIGHT" ]	[ 8, 8, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 8, 8, 10, 10 ]	[ [ "W", "AH0", "N" ], [ "W", "AH0", "N" ], [ "Z", "IH1", "ER0", "OW0" ], [ "EY0", "T" ] ]	[ [ 2, 1.8, 2 ], [ 2, 1.8, 2 ], [ 2, 1.4, 1.4, 2 ], [ 2, 2 ] ]	2.93
0026	6	f	000260033	THREE ONE SEVEN NINE	9	10	9	9	8	[ "THREE", "ONE", "SEVEN", "NINE" ]	[ 10, 8, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 8, 10, 10 ]	[ [ "TH", "R", "IY0" ], [ "W", "AH0", "N" ], [ "S", "EH1", "V", "N" ], [ "N", "AY0", "N" ] ]	[ [ 1.8, 2, 2 ], [ 2, 1.8, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ] ]	2.89
0026	6	f	000260039	EIGHT ZERO SIX TWO	9	10	9	9	9	[ "EIGHT", "ZERO", "SIX", "TWO" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "EY0", "T" ], [ "Z", "IH1", "ER0", "OW0" ], [ "S", "IH0", "K", "S" ], [ "T", "UW0" ] ]	[ [ 2, 2 ], [ 2, 1.6, 1.6, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ] ]	2.955
0026	6	f	000260048	THREE ZERO EIGHT SEVEN	9	10	9	9	8	[ "THREE", "ZERO", "EIGHT", "SEVEN" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "TH", "R", "IY0" ], [ "Z", "IH1", "ER0", "OW0" ], [ "EY0", "T" ], [ "S", "EH1", "V", "N" ] ]	[ [ 1.8, 2, 2 ], [ 2, 1.4, 1.4, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ] ]	2.552
0026	6	f	000260050	ZERO EIGHT THREE ONE	9	10	9	9	9	[ "ZERO", "EIGHT", "THREE", "ONE" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "Z", "IH1", "ER0", "OW0" ], [ "EY0", "T" ], [ "TH", "R", "IY0" ], [ "W", "AH0", "N" ] ]	[ [ 2, 2, 1.4, 2 ], [ 2, 2 ], [ 1.8, 2, 2 ], [ 2, 2, 2 ] ]	2.57
0026	6	f	000260052	SIX EIGHT SIX SEVEN	10	10	9	9	9	[ "SIX", "EIGHT", "SIX", "SEVEN" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "S", "IH0", "K", "S" ], [ "EY0", "T" ], [ "S", "IH0", "K", "S" ], [ "S", "EH1", "V", "N" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2 ] ]	3.48
0026	6	f	000260069	TEDDY GIVES UP SHOOTING	7	10	8	8	7	[ "TEDDY", "GIVES", "UP", "SHOOTING" ]	[ 5, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 6, 10, 10, 10 ]	[ [ "T", "EH1", "D", "IY0" ], [ "G", "IH0", "V", "Z" ], [ "AH0", "P" ], [ "SH", "UW1", "T", "IH0", "NG" ] ]	[ [ 2, 0.2, 2, 2 ], [ 2, 2, 2, 1.6 ], [ 2, 2 ], [ 2, 2, 2, 2, 2 ] ]	3.35
0026	6	f	000260075	HE LIKES RACING	9	10	9	8	8	[ "HE", "LIKES", "RACING" ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ 10, 10, 10 ]	[ [ "HH", "IY0" ], [ "L", "AY0", "K", "S" ], [ "R", "EY1", "S", "IH0", "NG" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2, 2 ] ]	2.41
0026	6	f	000260091	DORA LIKES YOUR RED JEANS	8	10	9	8	8	[ "DORA", "LIKES", "YOUR", "RED", "JEANS" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "D", "AO1", "R", "AH0" ], [ "L", "AY0", "K", "S" ], [ "Y", "UH", "AH0" ], [ "R", "EH0", "D" ], [ "JH", "IY0", "N", "Z" ] ]	[ [ 2, 2, 2, 1.4 ], [ 2, 2, 2, 1.6 ], [ 2, 1.8, 1.8 ], [ 2, 2, 2 ], [ 2, 2, 2, 1.6 ] ]	3.3
0026	6	f	000260095	BOB LOVES THE NEW CLOTH	8	10	9	9	8	[ "BOB", "LOVES", "THE", "NEW", "CLOTH" ]	[ 10, 10, 10, 10, 8 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 9, 10, 10, 8 ]	[ [ "B", "AH0", "B" ], [ "L", "AH0", "V", "Z" ], [ "DH", "AH0" ], [ "N", "Y", "UW0" ], [ "K", "L", "AH0", "TH" ] ]	[ [ 2, 1.8, 2 ], [ 2, 2, 2, 1.6 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 1, 2 ] ]	3.2
0026	6	f	000260096	JOHN NEEDS NEW T SHIRT	9	10	9	9	9	[ "JOHN", "NEEDS", "NEW", "T", "SHIRT" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "JH", "AH0", "N" ], [ "N", "IY0", "D", "Z" ], [ "N", "Y", "UW0" ], [ "T", "IY0" ], [ "SH", "ER0", "T" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	3.39
0026	6	f	000260112	THEN LILLY WALKS TO MUSIC ROOM	9	10	9	9	9	[ "THEN", "LILLY", "WALKS", "TO", "MUSIC", "ROOM" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "DH", "EH0", "N" ], [ "L", "IH1", "L", "IY0" ], [ "W", "AO0", "K", "S" ], [ "T", "UW0" ], [ "M", "Y", "UW1", "Z", "IH0", "K" ], [ "R", "UH0", "M" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 1.8, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2, 2, 2 ], [ 2, 2, 2 ] ]	3.88
0026	6	f	000260115	THEN TIM WALKS TO THEM PARK	9	10	8	8	8	[ "THEN", "TIM", "WALKS", "TO", "THEM", "PARK" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "DH", "EH0", "N" ], [ "T", "IH0", "M" ], [ "W", "AO0", "K", "S" ], [ "T", "UW0" ], [ "DH", "EH0", "M" ], [ "P", "AA0", "R", "K" ] ]	[ [ 1.6, 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 1.2, 2 ], [ 2, 2, 2, 2 ] ]	4.18
0026	6	f	000260121	THEN MANDY WALKS TO RESTROOM	9	10	9	9	9	[ "THEN", "MANDY", "WALKS", "TO", "RESTROOM" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "DH", "EH0", "N" ], [ "M", "AE1", "N", "D", "IY0" ], [ "W", "AO0", "K", "S" ], [ "T", "UW0" ], [ "R", "EH1", "S", "T", "R", "UH0", "M" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2, 2 ], [ 2, 1.8, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2, 2, 2, 2 ] ]	3.82
0026	6	f	000260126	DO YOU WANT THE PEA	9	10	9	9	9	[ "DO", "YOU", "WANT", "THE", "PEA" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "D", "UH0" ], [ "Y", "UW0" ], [ "W", "AA0", "N", "T" ], [ "DH", "AH0" ], [ "P", "IY0" ] ]	[ [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ] ]	2.38
0026	6	f	000260133	ANDY GOT THE MUSHROOM	10	10	9	9	9	[ "ANDY", "GOT", "THE", "MUSHROOM" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "AE0", "N", "D", "IH0" ], [ "G", "AH0", "T" ], [ "DH", "AH0" ], [ "M", "AH1", "SH", "R", "UH0", "M" ] ]	[ [ 2, 2, 2, 2 ], [ 2, 2, 1.8 ], [ 1.6, 2 ], [ 2, 2, 2, 2, 2, 2 ] ]	2.65
0026	6	f	000260166	NOW FOR THE PARTY	9	10	9	9	9	[ "NOW", "FOR", "THE", "PARTY" ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ 10, 10, 10, 10 ]	[ [ "N", "AW0" ], [ "F", "AO0", "R" ], [ "DH", "AH0" ], [ "P", "AA1", "R", "T", "IY0" ] ]	[ [ 2, 2 ], [ 2, 2, 2 ], [ 1.6, 2 ], [ 2, 2, 2, 2, 2 ] ]	2.56
0036	21	f	000360013	IT'S JUST SO HARD TO PICTURE	9	10	9	9	9	[ "IT'S", "JUST", "SO", "HARD", "TO", "PICTURE" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "IH0", "T", "S" ], [ "JH", "AH0", "S", "T" ], [ "S", "OW0" ], [ "HH", "AA0", "R", "D" ], [ "T", "UW0" ], [ "P", "IH1", "K", "CH", "ER0" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2, 2 ] ]	3.3
0036	21	f	000360034	WE SPEAK OUT WHEN WE FEEL WE SHOULD SPEAK OUT	9	10	9	8	8	[ "WE", "SPEAK", "OUT", "WHEN", "WE", "FEEL", "WE", "SHOULD", "SPEAK", "OUT" ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 10, 10 ]	[ [ "W", "IY0" ], [ "S", "P", "IY0", "K" ], [ "AW0", "T" ], [ "W", "EH0", "N" ], [ "W", "IY0" ], [ "F", "IY0", "L" ], [ "W", "IY0" ], [ "SH", "UH0", "D" ], [ "S", "P", "IY0", "K" ], [ "AW0", "T" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 2 ], [ 1.8, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 1.8, 2 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2, 2 ], [ 1.8, 2 ] ]	4.22
0036	21	f	000360036	I COULD DO WITH A BREAK	9	10	9	8	8	[ "I", "COULD", "DO", "WITH", "A", "BREAK" ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ [ "AY0" ], [ "K", "UH0", "D" ], [ "D", "UH0" ], [ "W", "IH0", "DH" ], [ "AH0" ], [ "B", "R", "EY0", "K" ] ]	[ [ 2 ], [ 2, 2, 2 ], [ 2, 1.6 ], [ 2, 2, 2 ], [ 2 ], [ 2, 2, 2, 2 ] ]	2.957
0036	21	f	000360132	IT IS ALWAYS SO NICE TO SEE YOU	9	10	9	8	8	[ "IT", "IS", "ALWAYS", "SO", "NICE", "TO", "SEE", "YOU" ]	[ 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10 ]	[ [ "IH0", "T" ], [ "IH0", "Z" ], [ "AO1", "L", "W", "EY0", "Z" ], [ "S", "OW0" ], [ "N", "AY0", "S" ], [ "T", "UW0" ], [ "S", "IY0" ], [ "Y", "UW0" ] ]	[ [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 1.8 ] ]	3.28
0036	21	f	000360133	UNFORTUNATELY NO ONE IS ALLOW TO SAY	8	10	9	8	8	[ "UNFORTUNATELY", "NO", "ONE", "IS", "ALLOW", "TO", "SAY" ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ [ "AH0", "N", "F", "AO1", "CH", "AH0", "N", "AH0", "T", "L", "IY0" ], [ "N", "OW0" ], [ "W", "AH0", "N" ], [ "IH0", "Z" ], [ "AH0", "L", "AW1" ], [ "T", "UW0" ], [ "S", "EY0" ] ]	[ [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 1.8 ], [ 2, 2, 1.8 ], [ 2, 2 ], [ 2, 2 ] ]	4.519
0036	21	f	000360161	ALMOST TIME FOR A HAIRCUT	7	10	9	8	7	[ "ALMOST", "TIME", "FOR", "A", "HAIRCUT" ]	[ 10, 10, 10, 10, 10 ]	[ 5, 10, 10, 10, 10 ]	[ 9, 10, 10, 10, 10 ]	[ [ "AO1", "L", "M", "OW0", "S", "T" ], [ "T", "AY0", "M" ], [ "F", "AO0" ], [ "AH0" ], [ "HH", "EH1", "R", "K", "AH0", "T" ] ]	[ [ 2, 2, 2, 2, 2, 1.8 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2 ], [ 2, 1.8, 1.8, 2, 2, 2 ] ]	3.089
0036	21	f	000360190	SO YOU WANT TO BE MORE PRODUCTIVE	9	10	8	7	8	[ "SO", "YOU", "WANT", "TO", "BE", "MORE", "PRODUCTIVE" ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ [ "S", "OW0" ], [ "Y", "UW0" ], [ "W", "AA0", "N", "T" ], [ "T", "UW0" ], [ "B", "IY0" ], [ "M", "AO0" ], [ "P", "R", "AH0", "D", "AH1", "K", "T", "IH0", "V" ] ]	[ [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 1.8 ], [ 2, 2 ], [ 2, 1.8 ], [ 2, 2 ], [ 2, 2, 1.8, 2, 2, 2, 2, 2, 2 ] ]	3.78
0036	21	f	000360210	STARTING CAN NOT BE BETTER THAN THIS	9	10	9	8	8	[ "STARTING", "CAN", "NOT", "BE", "BETTER", "THAN", "THIS" ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ [ "S", "T", "AA1", "R", "T", "IH0", "NG" ], [ "K", "AE0", "N" ], [ "N", "AH0", "T" ], [ "B", "IY0" ], [ "B", "EH1", "T", "ER0" ], [ "DH", "AE0", "N" ], [ "DH", "IH0", "S" ] ]	[ [ 2, 2, 2, 2, 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ] ]	3.268
0036	21	f	000360223	I KEEP IT TO BE AMUSED BY THE STUPIDITY	7	10	8	8	7	[ "I", "KEEP", "IT", "TO", "BE", "AMUSED", "BY", "THE", "STUPIDITY" ]	[ 10, 10, 10, 10, 10, 5, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 6, 10, 10, 10 ]	[ [ "AY0" ], [ "K", "IY0", "P" ], [ "IH0", "T" ], [ "T", "UW0" ], [ "B", "IY0" ], [ "AH0", "M", "Y", "UW1", "Z", "D" ], [ "B", "AY0" ], [ "DH", "AH0" ], [ "S", "T", "Y", "UW0", "P", "IH1", "D", "AH0", "T", "IY0" ] ]	[ [ 1.8 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 0.8, 1.6, 1.6, 0.8, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 1.8, 2, 2, 2, 2, 2, 2, 2 ] ]	4.021
0036	21	f	000360241	AFTER THAT I BE READY TO GO	8	10	8	8	8	[ "AFTER", "THAT", "I", "BE", "READY", "TO", "GO" ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ [ "AA1", "F", "T", "AH0" ], [ "DH", "AE0", "T" ], [ "AY0" ], [ "B", "IY0" ], [ "R", "EH1", "D", "IY0" ], [ "T", "UW0" ], [ "G", "OW0" ] ]	[ [ 2, 2, 2, 2 ], [ 1.6, 2, 2 ], [ 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 1.8 ] ]	3.24
0036	21	f	000360264	I THANK YOU FOR YOUR SYMPATHY BECAUSE IT COUNTS	7	10	8	8	7	[ "I", "THANK", "YOU", "FOR", "YOUR", "SYMPATHY", "BECAUSE", "IT", "COUNTS" ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 8 ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 8 ]	[ [ "AY0" ], [ "TH", "AE0", "NG", "K" ], [ "Y", "UW0" ], [ "F", "AO0" ], [ "Y", "AO0" ], [ "S", "IH1", "M", "P", "AH0", "TH", "IY0" ], [ "B", "IH0", "K", "AH1", "Z" ], [ "IH0", "T" ], [ "K", "AW0", "N", "T", "S" ] ]	[ [ 2 ], [ 1.4, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 1.6, 1.6, 1.6, 2 ], [ 2, 2, 2, 2, 1.8 ], [ 2, 2 ], [ 2, 1.8, 2, 2, 2 ] ]	4.556
0036	21	f	000360283	OFF THE CHAIN I BET	8	10	9	8	8	[ "OFF", "THE", "CHAIN", "I", "BET" ]	[ 10, 10, 10, 10, 8 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 8 ]	[ [ "AH0", "F" ], [ "DH", "AH0" ], [ "CH", "EY0", "N" ], [ "AY0" ], [ "B", "EH0", "T" ] ]	[ [ 2, 2 ], [ 1.4, 2 ], [ 2, 1.8, 2 ], [ 2 ], [ 2, 1, 2 ] ]	2.59
0036	21	f	000360313	HE NODDED HIS HEAD AND SMILED	8	10	9	8	8	[ "HE", "NODDED", "HIS", "HEAD", "AND", "SMILED" ]	[ 10, 5, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10 ]	[ 10, 6, 10, 10, 10, 10 ]	[ [ "HH", "IY0" ], [ "N", "AH1", "D", "IH0", "D" ], [ "HH", "IH0", "Z" ], [ "HH", "EH0", "D" ], [ "AE0", "N", "D" ], [ "S", "M", "AY0", "L", "D" ] ]	[ [ 2, 2 ], [ 2, 0.6, 2, 2, 2 ], [ 2, 2, 1.6 ], [ 2, 1.8, 2 ], [ 2, 2, 2 ], [ 2, 2, 1.6, 2, 2 ] ]	3.53
0036	21	f	000360314	BUT TELL ME THE TRUTH	9	10	9	8	8	[ "BUT", "TELL", "ME", "THE", "TRUTH" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "B", "AH0", "T" ], [ "T", "EH0", "L" ], [ "M", "IY0" ], [ "DH", "AH0" ], [ "T", "R", "UW0", "TH" ] ]	[ [ 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 2 ] ]	2.64
0036	21	f	000360332	THE FIRST QUESTION WAS AN OBVIOUS ONE	9	10	9	8	8	[ "THE", "FIRST", "QUESTION", "WAS", "AN", "OBVIOUS", "ONE" ]	[ 10, 10, 10, 8, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 8, 10, 10, 10 ]	[ [ "DH", "AH0" ], [ "F", "ER0", "S", "T" ], [ "K", "W", "EH1", "S", "CH", "AH0", "N" ], [ "W", "AH0", "Z" ], [ "AE0", "N" ], [ "AH1", "B", "V", "IH", "AH0", "S" ], [ "W", "AH0", "N" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2, 2, 2, 2, 2 ], [ 2, 1.8, 1.8 ], [ 2, 2 ], [ 2, 2, 1.2, 1.2, 1.2, 2 ], [ 2, 2, 2 ] ]	4.16
0036	21	f	000360334	THE SHOCK WAS TOO MUCH FOR HIM	9	10	9	8	8	[ "THE", "SHOCK", "WAS", "TOO", "MUCH", "FOR", "HIM" ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10 ]	[ [ "DH", "AH0" ], [ "SH", "AH0", "K" ], [ "W", "AH0", "Z" ], [ "T", "UW0" ], [ "M", "AH0", "CH" ], [ "F", "AO0" ], [ "HH", "IH0", "M" ] ]	[ [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 1.8 ], [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2 ], [ 2, 2, 2 ] ]	3.057
0036	21	f	000360339	HE KNOWS HE'S ABOUT TO GET INTO FIGHT	8	10	9	8	8	[ "HE", "KNOWS", "HE'S", "ABOUT", "TO", "GET", "INTO", "FIGHT" ]	[ 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10 ]	[ [ "HH", "IY0" ], [ "N", "OW0", "Z" ], [ "HH", "IY0", "Z" ], [ "AH0", "B", "AW1", "T" ], [ "T", "UW0" ], [ "G", "EH0", "T" ], [ "IH1", "N", "T", "UW0" ], [ "F", "AY0", "T" ] ]	[ [ 2, 2 ], [ 2, 2, 1.6 ], [ 2, 2, 1.6 ], [ 2, 2, 1.8, 2 ], [ 2, 2 ], [ 1.6, 2, 2 ], [ 2, 2, 2, 2 ], [ 2, 2, 2 ] ]	3.8
0036	21	f	000360343	HE WAS ALWAYS READY TO DO ANYTHING WHATEVER	9	10	9	8	8	[ "HE", "WAS", "ALWAYS", "READY", "TO", "DO", "ANYTHING", "WHATEVER" ]	[ 10, 10, 10, 10, 10, 10, 10, 8 ]	[ 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 8 ]	[ [ "HH", "IY0" ], [ "W", "AH0", "Z" ], [ "AO1", "L", "W", "EY0", "Z" ], [ "R", "EH1", "D", "IY0" ], [ "T", "UW0" ], [ "D", "UH0" ], [ "EH1", "N", "IY0", "TH", "IH0", "NG" ], [ "W", "AH0", "T", "EH1", "V", "ER0" ] ]	[ [ 2, 2 ], [ 2, 1.8, 2 ], [ 2, 2, 2, 2, 1.8 ], [ 2, 2, 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 2, 1.6, 2, 2 ], [ 2, 1.8, 2, 2, 1.4, 2 ] ]	4
0036	21	f	000360360	THE LONG AND THE SHORT OF IT IS THIS	7	10	9	8	7	[ "THE", "LONG", "AND", "THE", "SHORT", "OF", "IT", "IS", "THIS" ]	[ 10, 10, 10, 10, 10, 6, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10, 6, 10, 10, 10 ]	[ [ "DH", "AH0" ], [ "L", "AH0", "NG" ], [ "AE0", "N", "D" ], [ "DH", "AH0" ], [ "SH", "AO0", "T" ], [ "AH0", "V" ], [ "IH0", "T" ], [ "IH0", "Z" ], [ "DH", "IH0", "S" ] ]	[ [ 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ], [ 1.2, 1.2 ], [ 2, 1.8, 2 ], [ 2, 1.2 ], [ 2, 2 ], [ 2, 1.4 ], [ 2, 2, 2 ] ]	3.87
0036	21	f	000360378	YOU WANT TO BE LOVE	9	10	9	8	8	[ "YOU", "WANT", "TO", "BE", "LOVE" ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ 10, 10, 10, 10, 10 ]	[ [ "Y", "UW0" ], [ "W", "AH0", "N", "T" ], [ "T", "UW0" ], [ "B", "IY0" ], [ "L", "AH0", "V" ] ]	[ [ 2, 2 ], [ 2, 2, 2, 1.8 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2, 1.8 ] ]	2.26

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speechocean762: A non-native English corpus for pronunciation scoring task

How to use?

you can load data using

speechocean762_dataset = load_dataset('seba3y/speechocean762')

>> speechocean762_dataset
DatasetDict({
    train: Dataset({
        features: ['spk', 'age', 'gender', 'utt_name', 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total', 'words', 'words_accuracy', 'words_stress', 'words_total', 'phones', 'phones_godness'],
        num_rows: 2500
    })
    test: Dataset({
        features: ['spk', 'age', 'gender', 'utt_name', 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total', 'words', 'words_accuracy', 'words_stress', 'words_total', 'phones', 'phones_godness'],
        num_rows: 2500
    })
})

Features are ordered as following:

1- Demographic featurs: 'spk', 'age', 'gender', 'utt_name'

2- Sentence-level featurs: 'audio', 'utt_text', 'utt_accuracy', 'utt_completeness', 'utt_fluency', 'utt_prosodic', 'utt_total'

3- Word-level featurs: 'words', 'words_accuracy', 'words_stress', 'words_total'

4- Phoneme-level featurs: 'phones', 'phones_godness'

>> speechocean762_dataset['train'][0]

{'spk': '0001',
 'age': 6,
 'gender': 'm',
 'utt_name': '000010011',
 'audio': {'path': '/content/speechocean762/WAVE/SPEAKER0001/000010011.WAV',
  'array': array([-9.46044922e-04, -2.38037109e-03, -1.31225586e-03, ...,
         -9.15527344e-05,  3.05175781e-04, -2.44140625e-04]),
  'sampling_rate': 16000},
 'utt_text': 'WE CALL IT BEAR',
 'utt_accuracy': 8,
 'utt_completeness': 10.0,
 'utt_fluency': 9,
 'utt_prosodic': 9,
 'utt_total': 8,
 'words': "['WE', 'CALL', 'IT', 'BEAR']",
 'words_accuracy': '[10, 10, 10, 6]',
 'words_stress': '[10, 10, 10, 10]',
 'words_total': '[10, 10, 10, 6]',
 'phones': "[['W', 'IY0'], ['K', 'AO0', 'L'], ['IH0', 'T'], ['B', 'EH0', 'R']]",
 'phones_godness': '[[2.0, 2.0], [2.0, 1.8, 1.8], [2.0, 2.0], [2.0, 1.0, 1.0]]'}

For word-level features, the 'words' in each sample is a list containing words, while 'words_accuracy', 'words_stress', and 'words_total' are lists of the same length as the words. The mapping is such that the first word corresponds to the first value in 'words_accuracy', and so on. On the other hand, for phoneme-level features, the 'phones' in each sample is a 2D list, with each sublist corresponding to a single word

Introduction

Pronunciation scoring is a crucial technology in computer-assisted language learning (CALL) systems. The pronunciation quality scores might be given at phoneme-level, word-level, and sentence-level for a typical pronunciation scoring task.

This corpus aims to provide a free public dataset for the pronunciation scoring task. Key features:

It is available for free download for both commercial and non-commercial purposes.
The speaker variety encompasses young children and adults.
The manual annotations are in multiple aspects at sentence-level, word-level and phoneme-level.

This corpus consists of 5000 English sentences. All the speakers are non-native, and their mother tongue is Mandarin. Half of the speakers are Children, and the others are adults. The information of age and gender are provided.

Five experts made the scores. To avoid subjective bias, each expert scores independently under the same metric.

The scoring metric

The experts score at three levels: phoneme-level, word-level, and sentence-level.

Phoneme level

Score the pronunciation goodness of each phoneme within the words.

Score range: 0-2

2: pronunciation is correct
1: pronunciation is right but has a heavy accent
0: pronunciation is incorrect or missed

Word level

Score the accuracy and stress of each word's pronunciation.

Accuracy

Score range: 0 - 10

10: The pronunciation of the word is perfect
7-9: Most phones in this word are pronounced correctly but have accents
4-6: Less than 30% of phones in this word are wrongly pronounced
2-3: More than 30% of phones in this word are wrongly pronounced. In another case, the word is mispronounced as some other word. For example, the student mispronounced the word "bag" as "bike"
1: The pronunciation is hard to distinguish
0: no voice

Stress

Score range: {5, 10}

10: The stress is correct, or this is a mono-syllable word
5: The stress is wrong

Sentence level

Score the accuracy, fluency, completeness and prosodic at the sentence level.

Accuracy

Score range: 0 - 10

9-10: The overall pronunciation of the sentence is excellent, with accurate phonology and no obvious pronunciation mistakes
7-8: The overall pronunciation of the sentence is good, with a few pronunciation mistakes
5-6: The overall pronunciation of the sentence is understandable, with many pronunciation mistakes and accent, but it does not affect the understanding of basic meanings
3-4: Poor, clumsy and rigid pronunciation of the sentence as a whole, with serious pronunciation mistakes
0-2: Extremely poor pronunciation and only one or two words are recognizable

Completeness

Score range: 0.0 - 1.0 The percentage of the words with good pronunciation.

Fluency

Score range: 0 - 10

8-10: Fluent without noticeable pauses or stammering
6-7: Fluent in general, with a few pauses, repetition, and stammering
4-5: the speech is a little influent, with many pauses, repetition, and stammering
0-3: intermittent, very influent speech, with lots of pauses, repetition, and stammering

Prosodic

Score range: 0 - 10

9-10: Correct intonation at a stable speaking speed, speak with cadence, and can speak like a native
7-8: Nearly correct intonation at a stable speaking speed, nearly smooth and coherent, but with little stammering and few pauses
5-6: Unstable speech speed, many stammering and pauses with a poor sense of rhythm
3-4: Unstable speech speed, speak too fast or too slow, without the sense of rhythm
0-2: Poor intonation and lots of stammering and pauses, unable to read a complete sentence

Data structure

The following tree shows the file structure of this corpus on github:

├── scores.json
├── scores-detail.json
├── train
│   ├── spk2age
│   ├── spk2gender
│   ├── spk2utt
│   ├── text
│   ├── utt2spk
│   └── wav.scp
├── test
│   ├── spk2age
│   ├── spk2gender
│   ├── spk2utt
│   ├── text
│   ├── utt2spk
│   └── wav.scp
└── WAVE
    ├── SPEAKER0001
    │   ├── 000010011.WAV
    │   ├── 000010035.WAV
    │   ├── ...
    │   └── 000010173.WAV
    ├── SPEAKER0003
    │   ├── 000030012.WAV
    │   ├── 000030024.WAV
    │   ├── ...
    │   └── 000030175.WAV
    └── SPEAKER0005
        ├── 000050003.WAV
        ├── 000050010.WAV
        ├── ...
        └── 000050175.WAV

There are two datasets: train and test, and both are in Kaldi's data directory style.

The scores are stored in scores.json. Here is an example:

{
    "000010011": {                                     # utt-id
        "text": "WE CALL IT BEAR",                     # transcript text
        "accuracy": 8,                                 # sentence-level accuracy score
        "completeness": 10.0,                          # sentence-level completeness score
        "fluency": 9,                                  # sentence-level fluency score
        "prosodic": 9,                                 # sentence-level prosodic score
        "total": 8,                                    # sentence-level total score
        "words": [
            {
                "accuracy": 10,                        # word-level accuracy score
                "stress": 10,                          # word-level stress score
                "total": 10,                           # word-level total score
                "text": "WE",                          # the word text
                "phones": "W IY0",                     # phones of the word                        
                "phones-accuracy": [2.0, 2.0]          # phoneme-level accuracy score
            },
            {
                "accuracy": 10,
                "stress": 10,
                "total": 10,
                "text": "CALL",
                "phones": "K AO0 L",
                "phones-accuracy": [2.0, 1.8, 1.8]
            },
            {
                "accuracy": 10,
                "stress": 10,
                "total": 10,
                "text": "IT",
                "phones": "IH0 T",
                "phones-accuracy": [2.0, 2.0]
            },
            {
                "accuracy": 6,
                "stress": 10,
                "total": 6,
                "text": "BEAR",
                "phones": "B EH0 R",
                "phones-accuracy": [2.0, 1.0, 1.0]
            }
        ]
    },
    ...
}

For the phones with an accuracy score lower than 0.5, an extra "mispronunciations" block indicates which phoneme the current phone was actually pronounced. An example:

{
    "text": "LISA",
    "accuracy": 5,
    "phones": ["L", "IY1", "S", "AH0"],
    "phones-accuracy": [0.4, 2, 2, 1.2],
    "mispronunciations": [
        {
            "canonical-phone": "L",
            "index": 0,
            "pronounced-phone": "D"
        }
    ],
    "stress": 10,
    "total": 6
}

The file scores.json is processed from scores-detail.json. The two JSON files are almost the same, but scores-detail.json has the five experts' original scores, while the scores of scores.json were the average or median scores.

An example item in scores-detail.json:

{
    "000010011": {

        "text": "WE CALL IT BEAR",
        "accuracy": [7.0, 9.0, 8.0, 8.0, 9.0],
        "completeness": [1.0, 1.0, 1.0, 1.0, 1.0],
        "fluency": [10.0, 9.0, 8.0, 8.0, 10.0],
        "prosodic": [10.0, 9.0, 7.0, 8.0, 9.0],
        "total": [7.6, 9.0, 7.9, 8.0, 9.1],
        "words": [
            {
                "accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
                "stress": [10.0, 10.0, 10.0, 10.0, 10.0],
                "total": [10.0, 10.0, 10.0, 10.0, 10.0],
                "text": "WE",
                "ref-phones": "W IY0",
                "phones": ["W IY0", "W IY0", "W IY0", "W IY0", "W IY0"]
            },
            {
                "accuracy": [10.0, 8.0, 10.0, 10.0, 8.0],
                "stress": [10.0, 10.0, 10.0, 10.0, 10.0],
                "total": [10.0, 8.4, 10.0, 10.0, 8.4],
                "text": "CALL",
                "ref-phones": "K AO0 L",
                "phones": ["K AO0 L", "K {AO0} L", "K AO0 L", "K AO0 L", "K AO0 {L}"],
            },
            {
                "accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
                "stress": [10.0, 10.0, 10.0, 10.0, 10.0],
                "total": [10.0, 10.0, 10.0, 10.0, 10.0],
                "text": "IT",
                "ref-phones": "IH0 T",
                "phones": ["IH0 T", "IH0 T", "IH0 T", "IH0 T", "IH0 T"]
            },
            {
                "accuracy": [3.0, 7.0, 10.0, 2.0, 6.0],
                "stress": [10.0, 10.0, 10.0, 10.0, 10.0],
                "phones": ["B (EH0) (R)", "B {EH0} {R}", "B EH0 R", "B (EH0) (R)", "B EH0 [L] R"],
                "total": [4.4, 7.6, 10.0, 3.6, 6.8],
                "text": "BEAR",
                "ref-phones": "B EH0 R"
            }
        ],
    },
    ...
}

In scores-detail.json, the phoneme-level scores are notated in the following convenient notation:

for score 2, do not use any symbol
for score 1, use "{}" symbol
for score 0, use "()" symbol
for the inserted phone, use the "[]" symbol

For example, "B (EH) R" means the score of EH is 0 while the scores of B and R are both 2, "B EH [L] R" mean there is an unexpected phone "L" and the other phones are scored 2.

Citation

Please cite our paper if you find this work useful:

@inproceedings{zhang2021speechocean762,
  title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment},
  author={Zhang, Junbo and Zhang, Zhiwen and Wang, Yongqing and Yan, Zhiyong and Song, Qiong and Huang, Yukai and Li, Ke and Povey, Daniel and Wang, Yujun},
  booktitle={Proc. Interspeech 2021},
  year={2021}
}

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