Project Details
DARPA Low Resource Languages for Emergent Incidents (LORELEI) - Exploiting Language Information for Situational Awareness (ELISA)
Project Period: 1. 9. 2015 – 31. 3. 2020
Project Type: contract
Partner: University of Southern California
Speech processing, language, apeech mining
Speech processing in our proposal will be addressed by low-resource or
language-agnostic technologies. Rather than concentrating on mining the content
(for which, obviously, standard resources such as acoustic model, language model
or pronunciation dictionary will be lacking), speech data will be handled by
a multitude of "speech miners" that make minimum use of resources of the target
language. The processing will begin with a reliable voice activity detection
(VAD) capable of segmenting the signal into useful and useless portions. Often
regarded as "not a rocket science", a good VAD is crucial for correct functioning
of the following blocks and for human processing of speech input. Our work will
improve on existing DNN-based VAD that proved its efficiency in a difficult RATS
setting [Ng2012]. A processing with several phone posterior estimators with
either mono-lingual or multilingual phoneme sets [Schwarz2009] will follow to
provide the "miners" with a coherent low-dimensional representation. The first
real "miner" will be language identification (LID) with a significant set of
target languages (>60). Even if it is not sure that the target language will be
in this set, LID will allow to detect segments in English or possibly in other
languages for which we have ASR technology. We will follow our recent development
of LID base on features derived from phone posteriors [Plchot2013] as well as on
DNNs. We will also work on enrollment of a new language with very little data
(down to one utterance). Another "miner" will perform basic speaking style
recognition allowing to separate read speech from spontaneous. Finally, speaker
recognition (SRE) or clustering will allow to gather information about speakers
(in case they were previously enrolled) or at least to perform coarse speaker
clustering, as for the analyst, the information on who is speaking can be equally
important as what is said. Here, we will build up on our significant track in
iVector-based SRE and will mainly work on automatic adaptation and calibration on
unlabeled data-sets [Brummer2014]
Beneš Karel, Ing. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Fér Radek, Ing.
Glembek Ondřej, Ing., Ph.D.
Kocour Martin, Ing. (DCGM)
Matějka Pavel, Ing., Ph.D. (DCGM)
Ondel Lucas Antoine Francois, Mgr., Ph.D. (SSDIT)
Skácel Miroslav, Ing.
Szőke Igor, Ing., Ph.D. (DCGM)
Žmolíková Kateřina, Ing., Ph.D. (FIT)
2019
- ALAM, J.; BOULIANNE, G.; GLEMBEK, O.; LOZANO DÍEZ, A.; MATĚJKA, P.; MIZERA, P.; MONTEIRO, J.; MOŠNER, L.; NOVOTNÝ, O.; PLCHOT, O.; ROHDIN, J.; SILNOVA, A.; SLAVÍČEK, J.; STAFYLAKIS, T.; WANG, S.; ZEINALI, H. ABC NIST SRE 2019 CTS System Description. Proceedings of NIST. Sentosa, Singapore: National Institute of Standards and Technology, 2019.
p. 1-6. Detail - BASKAR, M.; WATANABE, S.; ASTUDILLO, R.; HORI, T.; BURGET, L.; ČERNOCKÝ, J. Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text. In Proceedings of Interspeech. Proceedings of Interspeech. Graz: International Speech Communication Association, 2019.
p. 3790-3794. ISSN: 1990-9772. Detail - MATĚJKA, P.; PLCHOT, O.; ZEINALI, H.; MOŠNER, L.; SILNOVA, A.; BURGET, L.; NOVOTNÝ, O.; GLEMBEK, O. Analysis of BUT Submission in Far-Field Scenarios of VOiCES 2019 Challenge. In Proceedings of Interspeech. Proceedings of Interspeech. Graz: International Speech Communication Association, 2019.
p. 2448-2452. ISSN: 1990-9772. Detail
2018
- ALAM, J.; BHATTACHARYA, G.; BRUMMER, J.; BURGET, L.; DIEZ SÁNCHEZ, M.; GLEMBEK, O.; KENNY, P.; KLČO, M.; LANDINI, F.; LOZANO DÍEZ, A.; MATĚJKA, P.; MONTEIRO, J.; MOŠNER, L.; NOVOTNÝ, O.; PLCHOT, O.; PROFANT, J.; ROHDIN, J.; SILNOVA, A.; SLAVÍČEK, J.; STAFYLAKIS, T.; ZEINALI, H. ABC NIST SRE 2018 SYSTEM DESCRIPTION. Proceedings of 2018 NIST SRE Workshop. Athens: National Institute of Standards and Technology, 2018.
p. 1-10. Detail - BENEŠ, K.; KESIRAJU, S.; BURGET, L. i-vectors in language modeling: An efficient way of domain adaptation for feed-forward models. In Proceedings of Interspeech 2018. Proceedings of Interspeech. Hyderabad: International Speech Communication Association, 2018.
p. 3383-3387. ISSN: 1990-9772. Detail - PULUGUNDLA, B.; BASKAR, M.; KESIRAJU, S.; EGOROVA, E.; KARAFIÁT, M.; BURGET, L.; ČERNOCKÝ, J. BUT system for low resource Indian language ASR. In Proceedings of Interspeech 2018. Proceedings of Interspeech. Hyderabad: International Speech Communication Association, 2018.
p. 3182-3186. ISSN: 1990-9772. Detail - WIESNER, M.; LIU, C.; ONDEL YANG, L.; HARMAN, C.; MANOHAR, V.; TRMAL, J.; HUANG, Z.; DEHAK, N.; KHUDANPUR, S. Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages. In Proceedings of Interspeech. Proceedings of Interspeech. Hyderabad: International Speech Communication Association, 2018.
p. 2052-2056. ISSN: 1990-9772. Detail
2017
- GLEMBEK, O. Summary report for project Exploiting Language Information for Situational Awareness (ELISA) For year 2017. Brno: University of Southern California, 2017.
p. 1-2. Detail - HANNEMANN, M.; TRMAL, J.; ONDEL YANG, L.; KESIRAJU, S.; BURGET, L. Bayesian joint-sequence models for grapheme-to-phoneme conversion. In Proceedings of ICASSP 2017. New Orleans: IEEE Signal Processing Society, 2017.
p. 2836-2840. ISBN: 978-1-5090-4117-6. Detail - KESIRAJU, S.; PAPPAGARI, R.; ONDEL YANG, L.; BURGET, L.; DEHAK, N.; KHUDANPUR, S.; ČERNOCKÝ, J.; GANGASHETTY, S. Topic identification of spoken documents using unsupervised acoustic unit discovery. In Proceedings of ICASSP 2017. New Orleans: IEEE Signal Processing Society, 2017.
p. 5745-5749. ISBN: 978-1-5090-4117-6. Detail - LIU, C.; YANG, J.; SUN, M.; KESIRAJU, S.; ROTT, A.; ONDEL YANG, L.; GHAHREMANI, P.; DEHAK, N.; BURGET, L.; KHUDANPUR, S. An Empirical evaluation of zero resource acoustic unit discovery. In Proceedings of ICASSP 2017. New Orleans: IEEE Signal Processing Society, 2017.
p. 5305-5309. ISBN: 978-1-5090-4117-6. Detail - ONDEL YANG, L.; BURGET, L.; ČERNOCKÝ, J.; KESIRAJU, S. Bayesian phonotactic language model for Acoustic Unit Discovery. In Proceedings of ICASSP 2017. New Orleans: IEEE Signal Processing Society, 2017.
p. 5750-5754. ISBN: 978-1-5090-4117-6. Detail - PAPADOPOULOS, P.; TRAVADI, R.; VAZ, C.; MALANDRAKIS, N.; HERMJAKOB, U.; POURDAMGHANI, N.; PUST, M.; ZHANG, B.; PAN, X.; LU, D.; LIN, Y.; GLEMBEK, O.; BASKAR, M.; KARAFIÁT, M.; BURGET, L.; HASEGAWA-JOHNSON, M.; JI, H.; MAY, J.; KNIGHT, K.; NARAYANAN, S. Team ELISA System for DARPA LORELEI Speech Evaluation 2016. In Proceedings of Interspeech 2017. Proceedings of Interspeech. Stockholm: International Speech Communication Association, 2017.
p. 2053-2057. ISSN: 1990-9772. Detail
2016
- GLEMBEK, O. Summary report for project Exploiting Language Information for Situational Awareness (ELISA) For year 2016. Brno: University of Southern California, 2016.
p. 1-2. Detail - KESIRAJU, S.; BURGET, L.; SZŐKE, I.; ČERNOCKÝ, J. Learning document representations using subspace multinomial model. In Proceedings of Interspeech 2016. San Francisco: International Speech Communication Association, 2016.
p. 700-704. ISBN: 978-1-5108-3313-5. Detail
2015
- GLEMBEK, O.; KESIRAJU, S.; ONDEL YANG, L. Summary report for project "ELISA" in Year 2015. Brno: University of Southern California, 2015.
p. 0-0. Detail