Non-native speech database

A non-native speech database is a speech database of non-native pronunciations of English. Such databases are used in the development of: multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers, and second language learning systems.

List

Table 1: Abbreviations for languages used in Table 2
Arabic A Japanese J Chinese C Korean K Czech Cze Malaysian M Danish D Norwegian N Dutch Dut Portuguese P English E Russian R French F Spanish S German G Swedish Swe Greek Gre Thai T Indonesian Ind Vietnamese V Italian I
Arabic	A	Japanese	J
Chinese	C	Korean	K
Czech	Cze	Malaysian	M
Danish	D	Norwegian	N
Dutch	Dut	Portuguese	P
English	E	Russian	R
French	F	Spanish	S
German	G	Swedish	Swe
Greek	Gre	Thai	T
Indonesian	Ind	Vietnamese	V
Italian	I

The actual table with information about the different databases is shown in Table 2.

Table 2: Overview of non-native Databases
Corpus	Author	Available at	Languages	#Speakers	Native Language	#Utt.	Duration	Date	Remarks
Corpus Author Available at Languages #Speakers Native Language #Utt. Duration Date Remarks AMI EU E Dut and other 100h meeting recordings ATR-Gruhn Gruhn ATR E 96 C G F J Ind 15000 2004 proficiency rating BAS Strange Corpus 1+10 ELRA G 139 50 countries 7500 1998 Berkeley Restaurant ICSI E 55 G I H C F S J 2500 1994 Broadcast News LDC E 1997 Cambridge-Witt Witt U. Cambridge E 10 J I K S 1200 1999 Cambridge-Ye Ye U. Cambridge E 20 C 1600 2005 Children News Tomokiyo CMU E 62 J C 7500 2000 partly spontaneous CLIPS-IMAG Tan CLIPS-IMAG F 15 C V 6h 2006 CLSU LDC E 22 countries 5000 2007 telephone, spontaneous CMU CMU E 64 G 452 0.9h not available Cross Towns Schaden U. Bochum E F G I Cze Dut 161 E F G I S 72000 133h 2006 city names Duke-Arslan Arslan Duke University E 93 15 countries 2200 1995 partly telephone speech ERJ Minematsu U. Tokyo E 200 J 68000 2002 proficiency rating Fischer LDC E many 200h telephone speech Fitt Fitt U. Edinburgh F I N Gre 10 E 700 1995 city names Fraenki U. Erlangen E 19 G 2148 Hispanic Byrne E 22 S 20h 1998 partly spontaneous HLTC HKUST E 44 C 3h 2010 available on request IBM-Fischer IBM E 40 S F G I 2000 2002 digits iCALL Chen I2R, ASTAR C 305 24 countries 90841 142h 2015 phonetic and tonal transcriptions (in Pinyin), proficiency ratings ISLE Atwell EU/ELDA E 46 G I 4000 18h 2000 Jupiter Zue MIT E unknown unknown 5146 1999 telephone speech K-SEC Rhee SiTEC E unknown K 2004 LDC WSJ1 LDC 10 800 1h 1994 LeaP Gut University of Münster E G 127 41 different ones 73.941 words 12h 2003 MIST ELRA E F G 75 Dut 2200 1996 NATO HIWIRE NATO E 81 F Gre I S 8100 2007 clean speech NATO M-ATC Pigeon NATO E 622 F G I S 9833 17h 2007 heavy background noise NATO N4 NATO E 115 unknown 7.5h 2006 heavy background noise Onomastica D Dut E F G Gre I N P S Swe (121000) 1995 only lexicon* PF-STAR U. Erlangen E 57 G 4627 3.4h 2005 children speech Sunstar EU E 100 G S I P D 40000 1992 parliament speech TC-STAR Heuvel ELDA E S unknown EU countries 13h 2006 multiple data sets TED Lamel ELDA E 40(188) many 10h(47h) 1994 eurospeech 93 TLTS DARPA A E 1h 2004 Tokyo-Kikuko U. Tokyo J 140 10 countries 35000 2004 proficiency rating Verbmobil U. Munich E 44 G 1.5h 1994 very spontaneous VODIS EU F G 178 F G 2500 1998 about car navigation WP Arabic Rocca LDC A 35 E 800 1h 2002 WP Russian Rocca LDC R 26 E 2500 2h 2003 WP Spanish Morgan LDC S E 2006 WSJ Spoke E 10 unknown 800 1993
AMI		EU	E		Dut and other		100h		meeting recordings
ATR-Gruhn	Gruhn	ATR	E	96	C G F J Ind	15000		2004	proficiency rating
BAS Strange Corpus 1+10		ELRA	G	139	50 countries	7500		1998
Berkeley Restaurant		ICSI	E	55	G I H C F S J	2500		1994
Broadcast News		LDC	E					1997
Cambridge-Witt	Witt	U. Cambridge	E	10	J I K S	1200		1999
Cambridge-Ye	Ye	U. Cambridge	E	20	C	1600		2005
Children News	Tomokiyo	CMU	E	62	J C	7500		2000	partly spontaneous
CLIPS-IMAG	Tan	CLIPS-IMAG	F	15	C V		6h	2006
CLSU		LDC	E		22 countries	5000		2007	telephone, spontaneous
CMU		CMU	E	64	G	452	0.9h		not available
Cross Towns	Schaden	U. Bochum	E F G I Cze Dut	161	E F G I S	72000	133h	2006	city names
Duke-Arslan	Arslan	Duke University	E	93	15 countries	2200		1995	partly telephone speech
ERJ	Minematsu	U. Tokyo	E	200	J	68000		2002	proficiency rating
Fischer		LDC	E		many		200h		telephone speech
Fitt	Fitt	U. Edinburgh	F I N Gre	10	E	700		1995	city names
Fraenki		U. Erlangen	E	19	G	2148
Hispanic	Byrne		E	22	S		20h	1998	partly spontaneous
HLTC		HKUST	E	44	C		3h	2010	available on request
IBM-Fischer		IBM	E	40	S F G I	2000		2002	digits
iCALL	Chen	I2R, A*STAR	C	305	24 countries	90841	142h	2015	phonetic and tonal transcriptions (in Pinyin), proficiency ratings
ISLE	Atwell	EU/ELDA	E	46	G I	4000	18h	2000
Jupiter	Zue	MIT	E	unknown	unknown	5146		1999	telephone speech
K-SEC	Rhee	SiTEC	E	unknown	K			2004
LDC WSJ1		LDC		10		800	1h	1994
LeaP	Gut	University of Münster	E G	127	41 different ones	73.941 words	12h	2003
MIST		ELRA	E F G	75	Dut	2200		1996
NATO HIWIRE		NATO	E	81	F Gre I S	8100		2007	clean speech
NATO M-ATC	Pigeon	NATO	E	622	F G I S	9833	17h	2007	heavy background noise
NATO N4		NATO	E	115	unknown		7.5h	2006	heavy background noise
Onomastica			D Dut E F G Gre I N P S Swe			(121000)		1995	only lexicon
PF-STAR		U. Erlangen	E	57	G	4627	3.4h	2005	children speech
Sunstar		EU	E	100	G S I P D	40000		1992	parliament speech
TC-STAR	Heuvel	ELDA	E S	unknown	EU countries		13h	2006	multiple data sets
TED	Lamel	ELDA	E	40(188)	many		10h(47h)	1994	eurospeech 93
TLTS		DARPA	A		E		1h	2004
Tokyo-Kikuko		U. Tokyo	J	140	10 countries	35000		2004	proficiency rating
Verbmobil		U. Munich	E	44	G		1.5h	1994	very spontaneous
VODIS		EU	F G	178	F G	2500		1998	about car navigation
WP Arabic	Rocca	LDC	A	35	E	800	1h	2002
WP Russian	Rocca	LDC	R	26	E	2500	2h	2003
WP Spanish	Morgan	LDC	S		E			2006
WSJ Spoke			E	10	unknown	800		1993

Legend

In the table of non-native databases some abbreviations for language names are used. They are listed in Table 1. Table 2 gives the following information about each corpus: The name of the corpus, the institution where the corpus can be obtained, or at least further information should be available, the language which was actually spoken by the speakers, the number of speakers, the native language of the speakers, the total amount of non-native utterances the corpus contains, the duration in hours of the non-native part, the date of the first public reference to this corpus, some free text highlighting special aspects of this database and a reference to another publication. The reference in the last field is in most cases to the paper which is especially devoted to describe this corpus by the original collectors. In some cases it was not possible to identify such a paper. In these cases a paper is referenced which is using this corpus is.

Some entries are left blank and others are marked with unknown. The difference here is that blank entries refer to attributes where the value is just not known. Unknown entries, however, indicate that no information about this attribute is available in the database itself. As an example, in the Jupiter weather database no information about the origin of the speakers is given. Therefore this data would be less useful for verifying accent detection or similar issues.

Where possible, the name is a standard name of the corpus, for some of the smaller corpora, however, there was no established name and hence an identifier had to be created. In such cases, a combination of the institution and the collector of the database is used.

In the case where the databases contain native and non-native speech, only attributes of the non-native part of the corpus are listed. Most of the corpora are collections of read speech. If the corpus instead consists either partly or completely of spontaneous utterances, this is mentioned in the Specials column.