The DOI matches its Crossref record; 335 of the 338 references it cites were found in a registry or an index.
Automatic speech recognition and speech variability: A review
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In the citing paper
“Previous work has shown that TTR and CD are expected to increase or remain constant as healthy individuals age [74], [75], [76].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [75]
Citations
Cites
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Flagged first, then by citations
3 of the 338 references couldn’t be matched to a known paper.
Research works printed but not linked: 15
- Beauford, J.Q., 1999. Compensating for variation in speaking rate, PhD thesis, Electrical Engineering, University of Pittsburgh.
- Bou-Ghazale, S.E., Hansen, J.L.H., 1995. Improving recognition and synthesis of stressed speech via feature perturbation in a source generator framework. In: ECSA-NATO Proceedings Speech Under Stress Workshop, Lisbon, Portugal, pp. 45–48.
- Das, S., Lubensky, D., Wu, C., 1999. Towards robust speech recognition in the telephony network environment – cellular and landline conditions. In: Proceedings of Eurospeech, Budapest, Hungary, pp. 1959–1962. · DOI
- Eide, E., Gish, H., 1996. A parametric approach to vocal tract length normalization. In: Proceedings of ICASSP, Atlanta, GA, pp. 346–349. · DOI
- ESCA Workshop on Modeling Pronunciation Variation for Automatic Speech Recognition, 1998.
- Eskenazi, M., Pelton, G., 2002. Pinpointing pronunciation errors in children speech: examining the role of the speech recognizer. In: Proceedings of the PMLA Workshop, Colorado, USA.
- Gales, M.J.F., 2001. Acoustic factorization. In: Proceedings of ASRU, Madona di Campiglio, Italy.
- Hansen, 1995, A source generator framework for analysis of acoustic correlates of speech under stress. part i: pitch, duration, and intensity effects, The Journal of the Acoustical Society of America
- Iivonen, A., Harinen, K., Keinanen, L., Kirjavainen, J., Meister, E., Tuuri, L., 2003. Development of a multiparametric speaker profile for speaker recognition. In: Proceedings of ICPhS, Barcelona, Spain, pp. 695–698.
- ISCA Tutorial and Research Workshop, 2002. Pronunciation Modeling and Lexicon Adaptation for Spoken Language Technology (PMLA-2002).
- Lombard, 1911, Le signe de l’élévation de la voix, Ann. Maladies Oreille, Larynx, Nez, Pharynx, 37
- Leonardo Neumeyer, Horacio Franco, Mitchel Weintraub, and Patti Price. 1996. Automatic text-independent pronunciation scoring of foreign language student speech. In: Proceedings of ICSLP, Philadelphia, PA, pp. 1457–1460. · DOI
- Selouani, S.-A., Tolba, H., O’Shaughnessy, D., 2002. Distinctive features, formants and cepstral coefficients to improve automatic speech recognition. In: Conference on Signal Processing, Pattern Recognition and Applications, IASTED. Crete, Greece, pp. 530–535.
- Uebler, U., Boros, M., 1999. Recognition of non-native german speech with multilingual recognizers. In: Proceedings of Eurospeech, vol. 2. Budapest, Hungary, pp. 911–914. · DOI
- Mitch Weintraub, Kelsey Taussig, Kate Hunicke-Smith, Amy Snodgrass. 1996. Effect of speaking style on LVCSR performance. In: Proceedings Addendum of ICSLP, Philadelphia, PA, USA.
Cited by
Sorted by citations
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993
Mel Frequency Cepstral Coefficient and its Applications: A Review
Abdul, Z. K., & Al‐Talabani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3223444 · Cited by 537
Cobot programming for collaborative industrial tasks: An overview
Zaatari, S. E., Marei, M., Li, W., & Usman, Z. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.03.003 · Cited by 470
Audio-driven facial animation by joint end-to-end learning of pose and emotion
Karras, T., Aila, T., Laine, S., Herva, A., & Lehtinen, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073658 · Cited by 435
Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing
Jung, Y.-H., Hong, S. K., Wang, H. S., Han, J. H., Pham, T. X., Park, H., Kim, J., Kang, S., Yoo, C. D., & Lee, K. J. (2019) · Advanced Materials · doi:10.1002/adma.201904020 · Cited by 344
Metadata
- Authors
- Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., Rose, R., Tyagi, V., & Wellekens, C.
- Affiliations
- Multitel (BE)
- Corresponding author
- Dupont, S.
- Venue
- Speech Communication
- Publisher
- Elsevier BV
- Published
- 2007-10
- Volume
- 49
- Issue
- 10-11
- Pages
- 763-786
- ISSN
- 0167-6393 (ISSN-L)
- OpenAlex
- W2168961642
- Cited by
- 529
- References
- 338
- Citations by year
- 2026: 18 · 2025: 33 · 2024: 40 · 2023: 37 · 2022: 50 · 2021: 33 · 2020: 29 · 2019: 23 · 2018: 25 · 2017: 24 · 2016: 22 · 2015: 33 · 2014: 27 · 2013: 30 · 2012: 34
- FWCI
- 17.51
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Speech Communication (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- PORTO@Iris (submittedVersion)
- ORBi UMONS (submittedVersion)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Funders
- Sixth Framework Programme (IST-2002-002034)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- speech recognition, speech variation, robustness, foreign accent, background noise, directory assistance, regional accents, sociolinguistic variation, speaker variability, speech rate, vocal effort, non-stationarity, limited training data, spoken language systems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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