The Concise Encyclopedia of Applied Linguistics. Carol A. Chapelle
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Название: The Concise Encyclopedia of Applied Linguistics

Автор: Carol A. Chapelle

Издательство: John Wiley & Sons Limited

Жанр: Языкознание

Серия:

isbn: 9781119147374

isbn:

СКАЧАТЬ (see Bernstein, Najmi, & Ehsani, 1999; Harless et al., 1999). O'Brien (2006) reviews a number of such programs. With the recent advent of deep learning systems, which take advantage of established techniques for modeling phone recognition and for making use of spectral information from speech, it is likely that machine feedback will soon become more accurate and flexible in L2 speech recognition.

      The connections between ASR and text‐to‐speech software have been insufficiently explored in applied linguistic circles, but both are regularly examined in cutting‐edge work tied to other areas of speech sciences. We expect that the ubiquity of mobile devices that use ASR‐based applications will eventually allow L2 learners to practice their L2 speaking skills and receive effective feedback on their pronunciation. Further progress in ASR will likely result in interactive language‐learning systems capable of providing authentic interaction opportunities with real or virtual interlocutors. These systems will also become able to produce specific, corrective feedback to learners on their pronunciation errors. Additionally, the development of noise‐robust ASR technologies will allow language learners to use ASR‐based products in noise‐prone environments such as classrooms, transportation, and other public places. Finally, the performance of ASR systems will improve as emotion recognition and visual speech recognition (based, for instance, on a Webcam's capturing of learners' lip movements and facial expressions) become more effective and widespread.

      SEE ALSO: Computer‐Assisted Pronunciation Teaching; Foreign Accent; Innovation in Language Teaching and Learning; Pronunciation Assessment; Pronunciation Teaching Methods and Techniques

      1 Anagnostopoulos, C.‐N., Iliou, T., & Glannoukos, I. (2015). Features and classifiers for emotion recognition from speech: A survey from 2000 to 2011. Artificial Intelligence Review, 43(2), 155–77.

      2 Anderson, J. N., Davidson, N., Morton, H., & Jack, M. A. (2008). Language learning with interactive virtual agent scenarios and speech recognition: Lessons learned. Computer Animation and Virtual Worlds, 19, 605–19.

      3 Bernstein, J., Najmi, A., & Ehsani, F. (1999). Subarashii: Encounters in Japanese spoken language education. CALICO Journal, 16(3), 361–84.

      4 Burileanu, D. (2008). Spoken language interfaces for embedded applications. In D. Gardner‐Bonneau & H. E. Blanchard (Eds.), Human factors and voice interactive systems (2nd ed., pp. 135–61). Norwell, MA: Springer.

      5 Cucchiarini, C., Neri, A., & Strik, H. (2009). Oral proficiency training in Dutch L2: The contribution of ASR‐based corrective feedback. Speech Communication, 51(10), 853–63.

      6 Dalby, J., & Kewley‐Port, D. (1999). Explicit pronunciation training using automatic speech recognition technology. CALICO Journal, 16(3), 425–45.

      7 Davis, K. H., Biddulph, R., & Balashek, S. (1952). Automatic recognition of spoken digits. The Journal of the Acoustical Society of America, 24(6), 637–42.

      8 Deng, L., Li, J., Huang, J.‐T., Yao, K., Yu, D., Seide, F., . . . & Acero, A. (2013). Recent advances in deep learning for speech research at Microsoft. In Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference (pp. 8604–8). Piscataway, NJ: IEEE.

      9 Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197–387.

      10 Derwing, T. M., Munro, M. J., & Carbonaro, M. (2000). Does popular speech recognition software work with ESL speech? TESOL Quarterly, 34, 592–603.

      11 Duan, R., Kawahara, T., Dantsuji, M., & Zhang, J. (2017). Effective articulatory modeling for pronunciation error detection of L2 learner without non‐native training data. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference (pp. 5815–19). Piscataway, NJ: IEEE.

      12 Eskenazi, M. (1999). Using a computer in foreign language pronunciation training: What advantages? CALICO Journal, 16(3), 447–69.

      13 Forgie, J. W., & Forgie, C. D. (1959). Results obtained from a vowel recognition computer program. The Journal of the Acoustical Society of America, 31(11), 1480–9.

      14 Harless, W., Zier, M., & Duncan, R. (1999). Virtual dialogues with native speakers: The evaluation of an interactive multimedia method. CALICO Journal, 16(3), 313–37.

      15 Lai, J., Karat, C.‐M., & Yankelovich, N. (2008). Conversational speech interfaces and technologies. In A. Sears & J. A. Jacko (Eds.), The human‐computer interaction handbook: Fundamentals, evolving technologies, and emerging applications (2nd ed., pp. 381–91). New York, NY: Erlbaum.

      16 Liakin, D., Cardoso, W., & Liakina, N. (2017). The pedagogical use of mobile speech synthesis (TTS): Focus on French liaison. Computer Assisted Language Learning, 30(3–4), 348–65.

      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.

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