Non-native speech database

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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. [1]

Contents

List

Table 1: Abbreviations for languages used in Table 2
ArabicAJapaneseJ
ChineseCKoreanK
CzechCzeMalaysianM
DanishDNorwegianN
DutchDutPortugueseP
EnglishERussianR
FrenchFSpanishS
GermanGSwedishSwe
GreekGreThaiT
IndonesianIndVietnameseV
ItalianI  


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

Table 2: Overview of non-native Databases
CorpusAuthorAvailable atLanguages#SpeakersNative Language#Utt.DurationDateRemarks
AMI [2] EUEDut and other100hmeeting recordings
ATR-Gruhn [3] GruhnATRE96C G F J Ind15000 2004proficiency rating
BAS Strange Corpus 1+10 [4]  ELRAG13950 countries7500 1998 
Berkeley Restaurant [5] ICSIE55G I H C F S J25001994 
Broadcast News [6]  LDCE    1997 
Cambridge-Witt [7] WittU. CambridgeE10J I K S1200 1999 
Cambridge-Ye [8] YeU. CambridgeE20C1600 2005 
Children News [9] TomokiyoCMUE62J C7500 2000partly spontaneous
CLIPS-IMAG [10] TanCLIPS-IMAGF15C V 6h2006 
CLSU [11]  LDCE 22 countries5000 2007telephone, spontaneous
CMU [12]  CMUE64G4520.9h not available
Cross Towns [13] SchadenU. BochumE F G I Cze Dut161E F G I S72000133h2006city names
Duke-Arslan [14] ArslanDuke UniversityE9315 countries2200 1995partly telephone speech
ERJ [15] MinematsuU. TokyoE200J68000 2002proficiency rating
Fischer [16] LDCEmany200htelephone speech
Fitt [17] FittU. EdinburghF I N Gre10E700 1995city names
Fraenki [18]  U. ErlangenE19G2148   
Hispanic [19] Byrne E22S 20h1998partly spontaneous
HLTC [20]  HKUSTE44C 3h2010available on request
IBM-Fischer [21]  IBME40S F G I2000 2002digits
iCALL [22] [23] ChenI2R, A*STARC30524 countries90841142h2015phonetic and tonal transcriptions (in Pinyin), proficiency ratings
ISLE [24] AtwellEU/ELDAE46G I400018h2000 
Jupiter [25] ZueMITEunknownunknown5146 1999telephone speech
K-SEC [26] RheeSiTECEunknownK  2004
LDC WSJ1 [27]  LDC 10 8001h1994 
LeaP [28] GutUniversity of MünsterE G12741 different ones73.941 words12h2003 
MIST [29]  ELRAE F G75Dut2200 1996 
NATO HIWIRE [30]  NATOE81F Gre I S8100 2007clean speech
NATO M-ATC [31] PigeonNATOE622F G I S983317h2007heavy background noise
NATO N4 [32]  NATOE115unknown 7.5h2006heavy background noise
Onomastica [33]   D Dut E F G Gre I N P S Swe (121000) 1995only lexicon
PF-STAR [34]  U. ErlangenE57G46273.4h2005children speech
Sunstar [35]  EUE100G S I P D40000 1992parliament speech
TC-STAR [36] HeuvelELDAE SunknownEU countries 13h2006multiple data sets
TED [37] LamelELDAE40(188)many 10h(47h)1994eurospeech 93
TLTS [38]  DARPAA E 1h2004 
Tokyo-Kikuko [39]  U. TokyoJ14010 countries35000 2004proficiency rating
Verbmobil [40]  U. MunichE44G 1.5h1994very spontaneous
VODIS [41]  EUF G178F G2500 1998about car navigation
WP Arabic [42] RoccaLDCA35E8001h2002 
WP Russian [43] RoccaLDCR26E25002h2003 
WP Spanish [44] MorganLDCS E  2006 
WSJ Spoke [45]   E10unknown800 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 [46] 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.

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References

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  10. ↑ T. P. Tan and L. Besacier, A French non-native corpus for automatic speech recognition, in LREC, Genoa, Italy, 2006.
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  22. ↑ Nancy F. Chen, Rong Tong, Darren Wee, Peixuan Lee, Bin Ma, Haizhou Li, iCALL Corpus: Mandarin Chinese Spoken by Non-Native Speakers of European Descent, in Proc. of Interspeech, 2015.
  23. ↑ Nancy F. Chen, Vivaek Shivakumar, Mahesh Harikumar, Bin Ma, Haizhou Li. Large-Scale Characterization of Mandarin Pronunciation Errors Made by native Speakers of European Languages, in Proc. of Interspeech, 2013.
  24. ↑ W. Menzel, E. Atwell, P. Bonaventura, D. Herron, P. Howarth, R. Morton, and C. Souter, The ISLE corpus of non-native spoken English, in LREC, Athens, Greece, 2000, pp. 957-963.
  25. ↑ K. Livescu, Analysis and modeling of non-native speech for automatic speech recognition, M.S. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1999.
  26. ↑ S-C. Rhee and S-H. Lee and S-K. Kang and Y-J. Lee, Design and Construction of Korean-Spoken English Corpus (K-SEC), Proc. ICSLP 2004
  27. ↑ L. Tomokiyo, Recognizing Non-native Speech: Characterizing and Adapting to Non-native Usage in Speech Recognition, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, 2001.
  28. ↑ Gut, U., Non-native Speech. A Corpus-based Analysis of Phonological and Phonetic Properties of L2 English and German, Frankfurt am Main: Peter Lang, 2009.
  29. ↑ TNO Human Factors Research Institute, Mist multi-lingual interoperability in speech technology database, Tech. Rep., ELRA, Paris, France, 2007, ELRA Catalog Reference S0238.
  30. ↑ J.C. Segura et al., The HIWIRE database, a noisy and non-native English speech corpus for cockpit communication, 2007, .
  31. ↑ S. Pigeon, W. Shen, and D. van Leeuwen, Design and characterization of the non-native military air traffic communications database, in ICSLP, Antwerp, Belgium, 2007.
  32. ↑ L. Benarousse et al., The NATO native and non-native (n4) speech corpus, in Proc. of the MIST workshop (ESCA-NATO), Leusden, Sep 1999.
  33. ↑ Onomastica Consortium, The ONOMASTICA interlanguage pronunciation lexicon, in Proc. Eurospeech, Madrid, Spain, 1995, pp. 829-832.
  34. ↑ C. Hacker, T. Cincarek, A. Maier, A. Hessler, and E. Noeth, Boosting of prosodic and pronunciation features to detect mispronunciations of non-native children, in Proc. of ICASSP, Honolulu, Hawai, 2007, pp. 197-200.
  35. ↑ C. Teixeira, I. Trancoso, and A. Serralheiro, Recognition of non-native accents, in Proc. Eurospeech, Rhodes, Greece, 1997, pp. 2375-2378.
  36. ↑ H. Heuvel, K. Choukri, C. Gollan, A. Moreno, and D. Mostefa, TC-STAR: New language resources for ASR and SLT purposes, in LREC, Genoa, 2006, pp. 2570-2573.
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  38. ↑ N. Mote, L. Johnson, A. Sethy, J. Silva, and S. Narayanan, Tactical language detection and modeling of learner speech errors: The case of Arabic tactical language training for American English speakers, in Proc. of InSTIL, June 2004.
  39. ↑ K. Nishina, Development of Japanese speech database read by non-native speakers for constructing CALL system, in ICA, Kyoto, Japan, 2004, pp. 561-564.
  40. ↑ University Munich, The Verbmobil project, .
  41. ↑ I. Trancoso, C. Viana, I. Mascarenhas, and C. Teixeira, On deriving rules for nativised pronunciation in navigation queries, in Proc. Eurospeech, 1999.
  42. ↑ A. LaRocca and R. Chouairi, West point Arabic speech corpus, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2002.
  43. ↑ A. LaRocca and C. Tomei, West point Russian speech corpus, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2003.
  44. ↑ J. Morgan, West point heroico Spanish speech, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2006.
  45. ↑ I. Amdal, F. Korkmazskiy, and A. C. Surendran, Joint pronunciation modelling of non-native speakers using data-driven methods, in ICSLP, Beijing, China, 2000, pp. 622-625.
  46. ↑ K. Livescu, Analysis and modeling of non-native speech for automatic speech recognition, M.S. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1999.
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