Non-native speech database

A non-native speech database is a speech database of non-native pronunciations of English. Such databases are essential for the ongoing development of multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers or even fully featured second language learning systems. Because of the comparably small size of the databases, however, many of them are not available through the common distributors of speech databases. This leads to the fact that it is hard for researchers in speech recognition to keep an overview of what kind of databases have already been collected, and for what purposes there are still no collections.[1]

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[2] 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.

Overview of non-native databases
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    


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
AMI [3] EU E Dut and other 100h meeting recordings
ATR-Gruhn [4] Gruhn ATR E 96 C G F J Ind 15000   2004 proficiency rating
BAS Strange Corpus 1+10 [5]   ELRA G 139 50 countries 7500   1998  
Berkeley Restaurant [6] ICSI E 55 G I H C F S J 2500 1994  
Broadcast News [7]   LDC E         1997  
Cambridge-Witt [8] Witt U. Cambridge E 10 J I K S 1200   1999  
Cambridge-Ye [9] Ye U. Cambridge E 20 C 1600   2005  
Children News [10] Tomokiyo CMU E 62 J C 7500   2000 partly spontaneous
CLIPS-IMAG [11] Tan CLIPS-IMAG F 15 C V   6h 2006  
CLSU [12]   LDC E   22 countries 5000   2007 telephone, spontaneous
CMU [13]   CMU E 64 G 452 0.9h   not available
Cross Towns [14] Schaden U. Bochum E F G I Cze Dut 161 E F G I S 72000 133h 2006 city names
Duke-Arslan [15] Arslan Duke University E 93 15 countries 2200   1995 partly telephone speech
ERJ [16] Minematsu U. Tokyo E 200 J 68000   2002 proficiency rating
Fischer [17] LDC E many 200h telephone speech
Fitt [18] Fitt U. Edinburgh F I N Gre 10 E 700   1995 city names
Fraenki [19]   U. Erlangen E 19 G 2148      
Hispanic [20] Byrne   E 22 S   20h 1998 partly spontaneous
HLTC [21]   HKUST E 44 C   3h 2010 available on request
IBM-Fischer [22]   IBM E 40 S F G I 2000   2002 digits
iCALL [23][24] Chen I2R, A*STAR C 305 24 countries 90841 142h 2015 phonetic and tonal transcriptions (in Pinyin), proficiency ratings
ISLE [25] Atwell EU/ELDA E 46 G I 4000 18h 2000  
Jupiter [26] Zue MIT E unknown unknown 5146   1999 telephone speech
K-SEC [27] Rhee SiTEC E unknown K     2004
LDC WSJ1 [28]   LDC   10   800 1h 1994  
LeaP [29] Gut University of Münster E G 127 41 different ones 73.941 words 12h 2003  
MIST [30]   ELRA E F G 75 Dut 2200   1996  
NATO HIWIRE [31]   NATO E 81 F Gre I S 8100   2007 clean speech
NATO M-ATC [32] Pigeon NATO E 622 F G I S 9833 17h 2007 heavy background noise
NATO N4 [33]   NATO E 115 unknown   7.5h 2006 heavy background noise
Onomastica [34]     D Dut E F G Gre I N P S Swe   (121000)   1995 only lexicon
PF-STAR [35]   U. Erlangen E 57 G 4627 3.4h 2005 children speech
Sunstar [36]   EU E 100 G S I P D 40000   1992 parliament speech
TC-STAR [37] Heuvel ELDA E S unknown EU countries   13h 2006 multiple data sets
TED [38] Lamel ELDA E 40(188) many   10h(47h) 1994 eurospeech 93
TLTS [39]   DARPA A   E   1h 2004  
Tokyo-Kikuko [40]   U. Tokyo J 140 10 countries 35000   2004 proficiency rating
Verbmobil [41]   U. Munich E 44 G   1.5h 1994 very spontaneous
VODIS [42]   EU F G 178 F G 2500   1998 about car navigation
WP Arabic [43] Rocca LDC A 35 E 800 1h 2002  
WP Russian [44] Rocca LDC R 26 E 2500 2h 2003  
WP Spanish [45] Morgan LDC S   E     2006  
WSJ Spoke [46]     E 10 unknown 800   1993  
gollark: What are you using "Rust" for anyway? osmarkscalculator™ contributions?
gollark: You're finally making Macron?!
gollark: But ææææ Rust.
gollark: I now assume it's just something like "the stack.push borrow lasts throughout the entire line".
gollark: My borrow checker model™ isn't actually great.

References
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  2. K. Livescu, Analysis and modeling of non-native speech for automatic speech recognition, M.S. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1999.
  3. AMI Project, "AMI Meeting Corpus" .
  4. R. Gruhn, T. Cincarek, and S. Nakamura, "A multi-accent non-native English database", in ASJ, 2004.
  5. University Munich, "Bavarian archive for speech signals strange corpus", .
  6. Jurafsky et al., "The Berkeley Restaurant Project", Proc. ICSLP 1994.
  7. L. Tomokiyo, Recognizing Non-native Speech: Characterizing and Adapting to Non-native Usage in Speech Recognition, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, 2001.
  8. S. Witt, Use of Speech Recognition in Computer-Assisted Language Learning, Ph.D. thesis, Cambridge University Engineering Department, UK, 1999.
  9. H. Ye and S. Young, Improving the speech recognition performance of beginners in spoken conversational interaction for language learning, in Proc. Interspeech, Lisbon, Portugal, 2005.
  10. L. Tomokiyo, Recognizing Non-native Speech: Characterizing and Adapting to Non-native Usage in Speech Recognition, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, 2001.
  11. T. P. Tan and L. Besacier, A French non-native corpus for automatic speech recognition, in LREC, Genoa, Italy, 2006.
  12. T. Lander, CSLU: Foreign accented English release 1.2, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2007.
  13. Z. Wang, T. Schultz, and A. Waibel, Comparison of acoustic model adaptation techniques on non-native speech, in Proc. ICASSP, 2003.
  14. S. Schaden, Regelbasierte Modellierung fremdsprachlich akzentbehafteter Aussprachevarianten, Ph.D. thesis, University Duisburg-Essen, 2006.
  15. L. M. Arslan and J. H. Hansen, Frequency characteristics of foreign accented speech, in Proc. of ICASSP, Munich, Germany, 1997, pp. 1123-1126.
  16. N. Minematsu et al., Development of English speech database read by Japanese to support CALL research, in ICA, Kyoto, Japan, 2004, pp. 577-560.
  17. Christopher Cieri, David Miller, Kevin Walker, The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text, Proc. LREC 2004
  18. S. Fitt, The pronunciation of unfamiliar native and non-native town names, in Proc. of Eurospeech, 1995, pp. 2227-2230.
  19. G. Stemmer, E. Noeth, and H. Niemann, Acoustic modeling of foreign words in a German speech recognition system, in Proc. Eurospeech, P. Dalsgaard, B. Lindberg, and H. Benner, Eds., 2001, vol. 4, pp. 2745-2748.
  20. W. Byrne, E. Knodt, S. Khudanpur, and J. Bernstein, Is automatic speech recognition ready for non-native speech? A data-collection effort and initial experiments in modeling conversational Hispanic English, in STiLL, Marholmen, Sweden, 1998, pp. 37-40.
  21. Y. Li, P. Fung, P. Xu, and Y. Liu, Asymmetric acoustic modeling for mixed language speech recognition, in ICASSP, Prague, Czech, 2011, pp. 37-40.
  22. V. Fischer, E. Janke, and S. Kunzmann, Recent progress in the decoding of non-native speech with multilingual acoustic models, in Proc. of Eurospeech, 2003, pp. 3105-3108.
  23. 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.
  24. 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.
  25. 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.
  26. K. Livescu, Analysis and modeling of non-native speech for automatic speech recognition, M.S. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1999.
  27. 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
  28. L. Tomokiyo, Recognizing Non-native Speech: Characterizing and Adapting to Non-native Usage in Speech Recognition, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, 2001.
  29. 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.
  30. TNO Human Factors Research Institute, Mist multi-lingual interoperability in speech technology database, Tech. Rep., ELRA, Paris, France, 2007, ELRA Catalog Reference S0238.
  31. J.C. Segura et al., The HIWIRE database, a noisy and non-native English speech corpus for cockpit communication, 2007, .
  32. 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.
  33. L. Benarousse et al., The NATO native and non-native (n4) speech corpus, in Proc. of the MIST workshop (ESCA-NATO), Leusden, Sep 1999.
  34. Onomastica Consortium, The ONOMASTICA interlanguage pronunciation lexicon, in Proc. Eurospeech, Madrid, Spain, 1995, pp. 829-832.
  35. 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.
  36. C. Teixeira, I. Trancoso, and A. Serralheiro, Recognition of non-native accents, in Proc. Eurospeech, Rhodes, Greece, 1997, pp. 2375-2378.
  37. 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.
  38. L.F. Lamel, F. Schiel, A. Fourcin, J. Mariani, and H. Tillmann, The translanguage English database TED, in ICSLP, Yokohama, Japan, Sep 1994.
  39. 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.
  40. K. Nishina, Development of Japanese speech database read by non-native speakers for constructing CALL system, in ICA, Kyoto, Japan, 2004, pp. 561-564.
  41. University Munich, The Verbmobil project, .
  42. I. Trancoso, C. Viana, I. Mascarenhas, and C. Teixeira, On deriving rules for nativised pronunciation in navigation queries, in Proc. Eurospeech, 1999.
  43. A. LaRocca and R. Chouairi, West point Arabic speech corpus, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2002.
  44. A. LaRocca and C. Tomei, West point Russian speech corpus, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2003.
  45. J. Morgan, West point heroico Spanish speech, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2006.
  46. 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.
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