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 bilingual language system. This more realistic view acknowledges that the frequent use of two languages produces a specific linguistic competence, one that differs from the competence of monolingual speaker–listeners but that is by no means inferior to it.
Most studies on language interaction (also called “crosslinguistic influence” or “transfer”) in bilinguals have looked at the influence of the native, first language (L1) on using the second (L2), ignoring the possibility that L2 may also influence L1. Laufer (2003) suggests one reason is that many researchers in applied linguistics have been especially interested in L2 learning, and particularly in its earliest stages. Crosslinguistic influences during these early stages of learning are almost entirely from the stronger L1 to the still weak L2 rather than from L2 to L1. A second reason she suggests is that much work on L2 learning has been motivated by the question of how members of immigrant communities can come to master the dominant language of the host community, the immigrants' L2, as rapidly as possible. Consequently, research primarily focused on how L2 was acquired rather than on what happened to L1 in the process. Whatever the reasons for the relative lack of studies examining an influence of L2 on L1, the available evidence indicates that such influence exists in all linguistic domains: phonology, lexicon, morphology, syntax, conceptual representation, and pragmatics (Pavlenko, 2000).
Language interaction in bilinguals is manifest in multiple phenomena. Among them are accented speech and accented comprehension, that is, the production of particular linguistic elements that differ from the way monolingual speakers would produce them, and differences in language comprehension processes as compared with monolinguals listening to or reading analogous language input. The word
