The Concise Encyclopedia of Applied Linguistics. Carol A. Chapelle
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17 Liew, A., & Wang, S. (2009). Visual speech recognition: Lip segmentation and mapping. Hershey, PA: Medical Information Science Reference.
18 Markowitz, J. A. (1996). Using speech recognition. Upper Saddle River, NJ: Prentice Hall.
19 Martin, T. B., Nelson, A. L., & Zadell, H. J. (1964). Speech recognition by feature abstraction techniques (Technical Report AL‐TDR‐64‐176). Wright‐Patterson Airforce Base, OH: Air Force Avionics Lab.
20 McCrocklin, S. M. (2016). Pronunciation learner autonomy: The potential of automatic speech recognition. System, 57, 25–42.
21 Mitra, V., Franco, H., Stern, R., Van Hout, J., Ferrer, L., Graciarena, M., . . . & Hansen, J. H. L. (2017). Robust features in deep learning‐based speech recognition. In S. Watanabe, M. Delcroix, F. Metze, & J. R. Hershey (Eds.), New era of robust speech recognition: Exploiting deep learning (pp. 187–217). Cham, Switzerland: Springer.
22 Mohamed, A., Dahl, G. E., & Hinton, G. (2012). Acoustic modeling using deep belief networks. IEEE Transactions on Audio, Speech, and Language Processing, 20(1), 14–22.
23 Mostow, J., & Aist, G. (1999). Giving help and praise in a reading tutor with imperfect listening—because automated speech recognition means never being able to say you're certain. CALICO Journal, 16(3), 407–24.
24 Neri, A., Cucchiarini, C., Strik, H., & Boves, L. (2002). The pedagogy‐technology interface in computer assisted pronunciation training. Computer‐Assisted Language Learning, 15(5), 441–67.
25 Neumeyer, L., Franco, H., Digalakis, V., & Weintraub, M. (2000). Automatic scoring of pronunciation quality. Speech Communication, 30, 83–93.
26 O'Brien, M. (2006). Teaching pronunciation and intonation with computer technology. In L. Ducate & N. Arnold (Eds.), Calling on CALL: From theory and research to new directions in foreign language teaching (pp. 127–48). San Marcos, Texas: Calico Monograph Series.
27 Pan, J., Liu, C., Wang, Z., Hu, Y., & Jiang, H. (2012). Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition: Why DNN surpasses GMMs in acoustic modeling. In The 8th International Symposium on Chinese Spoken Language Processing (ISCSLP), 301–5.
28 Peinado, A. M., & Segura, J. C. (2006). Speech recognition over digital channels: Robustness and standards. Chichester, England: John Wiley.
29 Poulsen, R., Hastings, P., & Allbritton, D. (2007). Tutoring bilingual students with an automated reading tutor that listens. Journal of Educational Computing Research, 36(2), 191–221.
30 Rabiner, L., & Juang, B.‐H. (1993). Fundamentals of speech recognition. Englewood Cliffs, NJ: Prentice Hall.
31 Reddy, D. (1966). An approach to computer speech recognition by direct analysis of the speech wave (Technical Report No. C549). Stanford, CA: Stanford University.
32 Rodman, R. D. (1999). Computer speech technology. Norwood, MA: Artech House.
33 Schuller, B., Batliner, A., Steidl, S., & Seppi, D. (2009). Emotion recognition from speech: Putting ASR in the loop. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘09) (pp. 4585–8). Taipei, Taiwan: IEEE.
34 Torkkola, K. (1994). Stochastic models and artificial neural networks for automatic speech recognition. In E. Keller (Ed.), Fundamentals of speech synthesis and speech recognition (pp. 149–69). Chichester, England: John Wiley.
35 Truong, K., Neri, A., de Wet, F., Cucchiarini, C., & Strik, H. (2005). Automatic detection of frequent pronunciation errors made by L2 learners. Proceedings of InterSpeech (pp. 1345–8). Lisbon, Portugal.
36 Vintsyuk, T. K. (1968). Speech discrimination by dynamic programming. Kibernetika, 4(2), 81–8.
37 Witt, S., & Young, S. (2000). Phone‐level pronunciation scoring and assessment for interactive language learning. Speech Communication, 30, 95–108.
38 Yu, D., & Deng, L. (2015). Automatic speech recognition: A deep learning approach. London, England: Springer.
39 Zhang, Z., Geiger, J., Pohjalainen, J., Mousa, A. E., Jin, W., & Schuller, B. (2017). Deep learning for environmentally robust speech recognition: An overview of recent developments. Retrieved April 3, 2019 from https://arxiv.org/abs/1705.10874
Suggested Readings
1 Hirose, K., & Tao, J. (Eds.). (2015). Speech prosody in speech synthesis: Modeling and generation of prosody for high quality and flexible speech synthesis. Berlin, Germany: Springer.
2 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–44.
3 Litman, D., Strik, H., & Lim, G. S. (2018). Speech technologies and the assessment of second language speaking: Approaches, challenges, and opportunities. Language Assessment Quarterly, 15(3), 294–309.
4 Saon, G., Kurata, G., Sercu, T., Audhkhasi, K., Thomas, S., Dimitriadis, D., . . . & Hall, P. (2017). English conversational telephone speech recognition by humans and machines. Retrieved April 3, 2019 from https://arxiv.org/pdf/1703.02136.pdf
5 Strik, H. (2012). ASR‐based systems for language learning and therapy. Retrieved April 3, 2019 from https://repository.ubn.ru.nl/bitstream/handle/2066/101719/101719.pdf
B Bilingualism and Cognition
ANNETTE M. B. DE GROOT
Scientific interest in the effects of (individual) bilingualism on cognition dates back to at least the first quarter of the 20th century, as illustrated by two early articles on the relation between bilingualism and mental development (Smith, 1923) and between bilingualism and intelligence (Saer, 1923). In addition to engaging scientists, the question of whether and how bilingualism affects cognition also concerns policy makers, educators, and parents of bilingual families. The widespread interest in this topic presumably stems from the desire to create circumstances that foster beneficial effects of bilingualism on cognitive functioning while at the same time preventing any adverse effects bilingualism might have. In one domain of cognition, namely, language representation and use, the influence of bilingualism is ubiquitous, affecting all components of the language system, but there is also plenty of evidence to suggest that bilingualism also affects nonlinguistic cognitive domains. In this entry the influence of bilingualism on both language (verbal cognition) and some aspects of nonverbal cognition is discussed.
Bilingualism and Language
Many studies have shown that a bilingual's two languages constantly interact with one another. It appears that even a purely unilingual communicative setting does not prevent the contextually inappropriate language from also being active and influencing the way in which the target language is processed. This holds for both language comprehension (e.g., Marian & Spivey, 2003) and language production (e.g., Starreveld, De Groot, Rossmark, & Van Hell, 2014), even when a bilingual's two languages do not share any orthographic or phonological relationship (e.g., English and Chinese; Wen, Filik, & Van Heuven, 2018), and when one language is spoken but the other is a sign language (Morford, Kroll, Piñar, & Wilkinson, 2014). The inevitable consequence of the inherently interactive nature of the bilingual language system is that the linguistic expressions of bilinguals differ from the analogous expressions of monolingual speakers. In other words, bilinguals do not equal two monolinguals in one person, and the linguistic expressions of monolinguals should not be considered the norm against which the language of bilinguals is evaluated. Contrary to such a “fractional” view of bilingualism, a “holistic” (Grosjean, 1989) or “multicompetence” (Cook & Li Wei, 2016) view of bilingualism acknowledges the inherently interactive nature of the