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.M. Raab, R. Gruhn and E. Noeth, Non-Native speech databases, in Proc. ASRU, Kyoto, Japan, 2007.
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+ 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 AMI Project, "AMI Meeting Corpus" . EU E Dut and other 100h meeting recordings ATR-Gruhn R. Gruhn, T. Cincarek, and S. Nakamura, "A multi-accent non-native English database", in ASJ, 2004. Gruhn ATR E 96 C G F J Ind 15000 2004 proficiency rating BAS Strange Corpus 1+10 Ludwig-Maximilians-Universität München, "Bavarian archive for speech signals strange corpus", . ELRA G 139 50 countries 7500 1998 Berkeley Restaurant Jurafsky et al., "The Berkeley Restaurant Project", Proc. ICSLP 1994. ICSI E 55 G I H C F S J 2500 1994 Broadcast News L. Tomokiyo, Recognizing Non-native Speech: Characterizing and Adapting to Non-native Usage in Speech Recognition, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, 2001. LDC E 1997 Cambridge-Witt S. Witt, Use of Speech Recognition in Computer-Assisted Language Learning, Ph.D. thesis, Cambridge University Engineering Department, UK, 1999. Witt U. Cambridge E 10 J I K S 1200 1999 Cambridge-Ye 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. Ye U. Cambridge E 20 C 1600 2005 Children News L. Tomokiyo, Recognizing Non-native Speech: Characterizing and Adapting to Non-native Usage in Speech Recognition, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, 2001. Tomokiyo CMU E 62 J C 7500 2000 partly spontaneous CLIPS-IMAG T. P. Tan and L. Besacier, A French non-native corpus for automatic speech recognition, in LREC, Genoa, Italy, 2006. Tan CLIPS-IMAG F 15 C V 6h 2006 CLSU T. Lander, CSLU: Foreign accented English release 1.2, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2007. LDC E 22 countries 5000 2007 telephone, spontaneous CMU Z. Wang, T. Schultz, and A. Waibel, Comparison of acoustic model adaptation techniques on non-native speech, in Proc. ICASSP, 2003. CMU E 64 G 452 0.9h not available Cross Towns S. Schaden, Regelbasierte Modellierung fremdsprachlich akzentbehafteter Aussprachevarianten, Ph.D. thesis, University Duisburg-Essen, 2006. Schaden U. Bochum E F G I Cze Dut 161 E F G I S 72000 133h 2006 city names Duke-Arslan L. M. Arslan and J. H. Hansen, Frequency characteristics of foreign accented speech, in Proc. of ICASSP, Munich, Germany, 1997, pp. 1123-1126. Arslan Duke University E 93 15 countries 2200 1995 partly telephone speech ERJ N. Minematsu et al., Development of English speech database read by Japanese to support CALL research, in ICA, Kyoto, Japan, 2004, pp. 577-560. Minematsu U. Tokyo E 200 J 68000 2002 proficiency rating Fischer Christopher Cieri, David Miller, Kevin Walker, The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text, Proc. LREC 2004 LDC E many 200h telephone speech Fitt S. Fitt, The pronunciation of unfamiliar native and non-native town names, in Proc. of Eurospeech, 1995, pp. 2227-2230. Fitt U. Edinburgh F I N Gre 10 E 700 1995 city names Fraenki 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. U. Erlangen E 19 G 2148 Hispanic 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. Byrne E 22 S 20h 1998 partly spontaneous HLTC 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. HKUST E 44 C 3h 2010 available on request IBM-Fischer 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. IBM E 40 S F G I 2000 2002 digits iCALL 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.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. Chen I2R, A*STAR C 305 24 countries 90841 142h 2015 phonetic and tonal transcriptions (in Pinyin), proficiency ratings ISLE 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. Atwell EU/ELDA E 46 G I 4000 18h 2000 Jupiter K. Livescu, Analysis and modeling of non-native speech for automatic speech recognition, M.S. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1999. Zue MIT E unknown unknown 5146 1999 telephone speech K-SEC 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 Rhee SiTEC E unknown K 2004 LDC WSJ1 L. Tomokiyo, Recognizing Non-native Speech: Characterizing and Adapting to Non-native Usage in Speech Recognition, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, 2001. LDC 10 800 1h 1994 LeaP 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. Gut University of Münster E G 127 41 different ones 73.941 words 12h 2003 MIST TNO Human Factors Research Institute, Mist multi-lingual interoperability in speech technology database, Tech. Rep., ELRA, Paris, France, 2007, ELRA Catalog Reference S0238. ELRA E F G 75 Dut 2200 1996 NATO HIWIRE J.C. Segura et al., The HIWIRE database, a noisy and non-native English speech corpus for cockpit communication, 2007, . NATO E 81 F Gre I S 8100 2007 clean speech NATO M-ATC 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. Pigeon NATO E 622 F G I S 9833 17h 2007 heavy background noise NATO N4 L. Benarousse et al., The NATO native and non-native (n4) speech corpus, in Proc. of the MIST workshop (ESCA-NATO), Leusden, Sep 1999. NATO E 115 unknown 7.5h 2006 heavy background noise Onomastica Onomastica Consortium, The ONOMASTICA interlanguage pronunciation lexicon, in Proc. Eurospeech, Madrid, Spain, 1995, pp. 829-832. D Dut E F G Gre I N P S Swe (121000) 1995 only lexicon PF-STAR 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. U. Erlangen E 57 G 4627 3.4h 2005 children speech Sunstar C. Teixeira, I. Trancoso, and A. Serralheiro, Recognition of non-native accents, in Proc. Eurospeech, Rhodes, Greece, 1997, pp. 2375-2378. EU E 100 G S I P D 40000 1992 parliament speech TC-STAR 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. Heuvel ELDA E S unknown EU countries 13h 2006 multiple data sets TED L.F. Lamel, F. Schiel, A. Fourcin, J. Mariani, and H. Tillmann, The translanguage English database TED, in ICSLP, Yokohama, Japan, Sep 1994. Lamel ELDA E 40(188) many 10h(47h) 1994 eurospeech 93 TLTS 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. DARPA A E 1h 2004 Tokyo-Kikuko K. Nishina, Development of Japanese speech database read by non-native speakers for constructing CALL system, in ICA, Kyoto, Japan, 2004, pp. 561-564. U. Tokyo J 140 10 countries 35000 2004 proficiency rating Verbmobil Ludwig-Maximilians-Universität München, The Verbmobil project, . U. Munich E 44 G 1.5h 1994 very spontaneous VODIS I. Trancoso, C. Viana, I. Mascarenhas, and C. Teixeira, On deriving rules for nativised pronunciation in navigation queries, in Proc. Eurospeech, 1999. EU F G 178 F G 2500 1998 about car navigation WP Arabic A. LaRocca and R. Chouairi, West point Arabic speech corpus, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2002. Rocca LDC A 35 E 800 1h 2002 WP Russian A. LaRocca and C. Tomei, West point Russian speech corpus, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2003. Rocca LDC R 26 E 2500 2h 2003 WP Spanish J. Morgan, West point heroico Spanish speech, Tech. Rep., LDC, Philadelphia, Pennsylvania, 2006. Morgan LDC S E 2006 WSJ Spoke 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. 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 databaseK. Livescu, Analysis and modeling of non-native speech for automatic speech recognition, M.S. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1999. 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.