Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations

Query-by-example spoken term detection (QbE STD) aims at retrieving data from a speech repository given an acoustic query containing the term of interest as input. Nowadays, it is receiving much interest due to the large volume of multimedia information. This paper presents the systems submitted to the ALBAYZIN QbE STD 2014 evaluation held as a part of the ALBAYZIN 2014 Evaluation campaign within the context of the IberSPEECH 2014 conference. This is the second QbE STD evaluation in Spanish, which allows us to evaluate the progress in this technology for this language. The evaluation consists in retrieving the speech files that contain the input queries, indicating the start and end times where the input queries were found, along with a score value that reflects the confidence given to the detection of the query. Evaluation is conducted on a Spanish spontaneous speech database containing a set of talks from workshops, which amount to about 7 h of speech. We present the database, the evaluation metric, the systems submitted to the evaluation, the results, and compare this second evaluation with the first ALBAYZIN QbE STD evaluation held in 2012. Four different research groups took part in the evaluations held in 2012 and 2014. In 2014, new multi-word and foreign queries were added to the single-word and in-language queries used in 2012. Systems submitted to the second evaluation are hybrid systems which integrate letter transcription- and template matching-based systems. Despite the significant improvement obtained by the systems submitted to this second evaluation compared to those of the first evaluation, results still show the difficulty of this task and indicate that there is still room for improvement.This research was funded by the Spanish Government ('SpeechTech4All Project' TEC2012 38939 C03 01 and 'CMC-V2 Project' TEC2012 37585 C02 01), the Galician Government through the research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014) and 'AtlantTIC Project' CN2012/160, and also by the Spanish Government and the European Regional Development Fund (ERDF) under project TACTICA

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

[EN] Query-by-example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given an acoustic (spoken) query containing the term of interest as the input. This paper presents the systems submitted to the ALBAYZIN QbE STD 2016 Evaluation held as a part of the ALBAYZIN 2016 Evaluation Campaign at the IberSPEECH 2016 conference. Special attention was given to the evaluation design so that a thorough post-analysis of the main results could be carried out. Two different Spanish speech databases, which cover different acoustic and language domains, were used in the evaluation: the MAVIR database, which consists of a set of talks from workshops, and the EPIC database, which consists of a set of European Parliament sessions in Spanish. We present the evaluation design, both databases, the evaluation metric, the systems submitted to the evaluation, the results, and a thorough analysis and discussion. Four different research groups participated in the evaluation, and a total of eight template matching-based systems were submitted. We compare the systems submitted to the evaluation and make an in-depth analysis based on some properties of the spoken queries, such as query length, single-word/multi-word queries, and in-language/out-of-language queries.This work was partially supported by Fundacao para a Ciencia e Tecnologia (FCT) under the projects UID/EEA/50008/2013 (pluriannual funding in the scope of the LETSREAD project) and UID/CEC/50021/2013, and Grant SFRH/BD/97187/2013. Jorge Proenca is supported by the SFRH/BD/97204/2013 FCT Grant. This work was also supported by the Galician Government ('Centro singular de investigacion de Galicia' accreditation 2016-2019 ED431G/01 and the research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014)), the European Regional Development Fund (ERDF), the projects "DSSL: Redes Profundas y Modelos de Subespacios para Deteccion y Seguimiento de Locutor, Idioma y Enfermedades Degenerativas a partir de la Voz" (TEC2015-68172-C2-1-P) and the TIN2015-64282-R funded by Ministerio de Economia y Competitividad in Spain, the Spanish Government through the project "TraceThem" (TEC2015-65345-P), and AtlantTIC ED431G/04.Tejedor, J.; Toledano, DT.; Lopez-Otero, P.; Docio-Fernandez, L.; Proença, J.; Perdigão, F.; García-Granada, F.... (2018). ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation. EURASIP Journal on Audio, Speech and Music Processing. 1-25. https://doi.org/10.1186/s13636-018-0125-9S125Jarina, R, Kuba, M, Gubka, R, Chmulik, M, Paralic, M (2013). UNIZA system for the spoken web search task at MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 791–792).Ali, A, & Clements, MA (2013). Spoken web search using and ergodic hidden Markov model of speech. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 861–862).Buzo, A, Cucu, H, Burileanu, C (2014). SpeeD@MediaEval 2014: Spoken term detection with robust multilingual phone recognition. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 721–722).Caranica, A, Buzo, A, Cucu, H, Burileanu, C (2015). SpeeD@MediaEval 2015: Multilingual phone recognition approach to Query By Example STD. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 781–783).Kesiraju, S, Mantena, G, Prahallad, K (2014). IIIT-H system for MediaEval 2014 QUESST. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 761–762).Ma, M, & Rosenberg, A (2015). CUNY systems for the Query-by-Example search on speech task at MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 831–833).Takahashi, J, Hashimoto, T, Konno, R, Sugawara, S, Ouchi, K, Oshima, S, Akyu, T, Itoh, Y (2014). An IWAPU STD system for OOV query terms and spoken queries. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 384–389).Makino, M, & Kai, A (2014). Combining subword and state-level dissimilarity measures for improved spoken term detection in NTCIR-11 SpokenQuery & Doc task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 413–418).Konno, R, Ouchi, K, Obara, M, Shimizu, Y, Chiba, T, Hirota, T, Itoh, Y (2016). An STD system using multiple STD results and multiple rescoring method for NTCIR-12 SpokenQuery & Doc task. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 200–204).Sakamoto, N, Yamamoto, K, Nakagawa, S (2015). Combination of syllable based N-gram search and word search for spoken term detection through spoken queries and IV/OOV classification. In Proc. of ASRU. IEEE, New York, (pp. 200–206).Hou, J, Pham, VT, Leung, C-C, Wang, L, 2, HX, Lv, H, Xie, L, Fu, Z, Ni, C, Xiao, X, Chen, H, Zhang, S, Sun, S, Yuan, Y, Li, P, Nwe, TL, Sivadas, S, Ma, B, Chng, ES, Li, H (2015). The NNI Query-by-Example system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 141–143).Vavrek, J, Viszlay, P, Lojka, M, Pleva, M, Juhar, J, Rusko, M (2015). TUKE at MediaEval 2015 QUESST. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 451–453).Mantena, G, Achanta, S, Prahallad, K (2014). Query-by-example spoken term detection using frequency domain linear prediction and non-segmental dynamic time warping. IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(5), 946–955.Anguera, X, & Ferrarons, M (2013). Memory efficient subsequence DTW for query-by-example spoken term detection. In Proc. of ICME. IEEE, New York, (pp. 1–6).Tulsiani, H, & Rao, P (2015). The IIT-B Query-by-Example system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 341–343).Bouallegue, M, Senay, G, Morchid, M, Matrouf, D, Linares, G, Dufour, R (2013). LIA@MediaEval 2013 spoken web search task: An I-Vector based approach. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 771–772).Rodriguez-Fuentes, LJ, Varona, A, Penagarikano, M, Bordel, G, Diez, M (2013). GTTS systems for the SWS task at MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 831–832).Wang, H, Lee, T, Leung, C-C, Ma, B, Li, H (2013). Using parallel tokenizers with DTW matrix combination for low-resource spoken term detection. In Proc. of ICASSP. IEEE, New York, (pp. 8545–8549).Wang, H, & Lee, T (2013). The CUHK spoken web search system for MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 681–682).Proenca, J, Veiga, A, Perdigão, F (2014). The SPL-IT query by example search on speech system for MediaEval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 741–742).Proenca, J, Veiga, A, Perdigao, F (2015). Query by example search with segmented dynamic time warping for non-exact spoken queries. In Proc. of EUSIPCO. Springer, Berlin, (pp. 1691–1695).Proenca, J, Castela, L, Perdigao, F (2015). The SPL-IT-UC Query by Example search on speech system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 471–473).Proenca, J, & Perdigao, F (2016). Segmented dynamic time warping for spoken Query-by-Example search. In Proc. of Interspeech. ISCA, Baixas, (pp. 750–754).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2015). GTM-UVigo systems for the Query-by-Example search on speech task at MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 521–523).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2015). Phonetic unit selection for cross-lingual Query-by-Example spoken term detection. In Proc. of ASRU. IEEE, New York, (pp. 223–229).Saxena, A, & Yegnanarayana, B (2015). Distinctive feature based representation of speech for Query-by-Example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 3680–3684).Skacel, M, & Szöke, I (2015). BUT QUESST 2015 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 721–723).Chen, H, Leung, C-C, Xie, L, Ma, B, Li, H (2016). Unsupervised bottleneck features for low-resource Query-by-Example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 923–927).Yuan, Y, Leung, C-C, Xie, L, Chen, H, Ma, B, Li, H (2017). Pairwise learning using multi-lingual bottleneck features for low-resource Query-by-Example spoken term detection. In Proc. of ICASSP. IEEE, New York, (pp. 5645–5649).Torbati, AHHN, & Picone, J (2016). A nonparametric Bayesian approach for spoken term detection by example query. In Proc. of Interspeech. ISCA, Baixas, (pp. 928–932).Popli, A, & Kumar, A (2015). Query-by-example spoken term detection using low dimensional posteriorgrams motivated by articulatory classes. In Proc. of MMSP. IEEE, New York, (pp. 1–6).Yang, P, Leung, C-C, Xie, L, Ma, B, Li, H (2014). Intrinsic spectral analysis based on temporal context features for query-by-example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 1722–1726).George, B, Saxena, A, Mantena, G, Prahallad, K, Yegnanarayana, B (2014). Unsupervised query-by-example spoken term detection using bag of acoustic words and non-segmental dynamic time warping. In Proc. of Interspeech. ISCA, Baixas, (pp. 1742–1746).Hazen, TJ, Shen, W, White, CM (2009). Query-by-example spoken term detection using phonetic posteriorgram templates. In Proc. of ASRU. IEEE, New York, (pp. 421–426).Abad, A, Astudillo, RF, Trancoso, I (2013). The L2F spoken web search system for mediaeval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 851–852).Szöke, I, Skácel, M, Burget, L (2014). BUT QUESST 2014 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 621–622).Szöke, I, Burget, L, Grézl, F, Černocký, JH, Ondel, L (2014). Calibration and fusion of query-by-example systems - BUT SWS 2013. In Proc. of ICASSP. IEEE, New York, (pp. 621–622).Abad, A, Rodríguez-Fuentes, LJ, Penagarikano, M, Varona, A, Bordel, G (2013). On the calibration and fusion of heterogeneous spoken term detection systems. In Proc. of Interspeech. ISCA, Baixas, (pp. 20–24).Yang, P, Xu, H, Xiao, X, Xie, L, Leung, C-C, Chen, H, Yu, J, Lv, H, Wang, L, Leow, SJ, Ma, B, Chng, ES, Li, H (2014). The NNI query-by-example system for MediaEval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 691–692).Leung, C-C, Wang, L, Xu, H, Hou, J, Pham, VT, Lv, H, Xie, L, Xiao, X, Ni, C, Ma, B, Chng, ES, Li, H (2016). Toward high-performance language-independent Query-by-Example spoken term detection for MediaEval 2015: Post-evaluation analysis. In Proc. of Interspeech. ISCA, Baixas, (pp. 3703–3707).Xu, H, Hou, J, Xiao, X, Pham, VT, Leung, C-C, Wang, L, Do, VH, Lv, H, Xie, L, Ma, B, Chng, ES, Li, H (2016). Approximate search of audio queries by using DTW with phone time boundary and data augmentation. In Proc. of ICASSP. IEEE, New York, (pp. 6030–6034).Oishi, S, Matsuba, T, Makino, M, Kai, A (2016). Combining state-level and DNN-based acoustic matches for efficient spoken term detection in NTCIR-12 SpokenQuery &Doc-2 task. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 205–210).Oishi, S, Matsuba, T, Makino, M, Kai, A (2016). Combining state-level spotting and posterior-based acoustic match for improved query-by-example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 740–744).Obara, M, Kojima, K, Tanaka, K, Lee, S-w, Itoh, Y (2016). Rescoring by combination of posteriorgram score and subword-matching score for use in Query-by-Example. In Proc. of Interspeech. ISCA, Baixas, (pp. 1918–1922).NIST. The Ninth Text REtrieval Conference (TREC 9). http://trec.nist.gov . Accessed Feb 2018.Anguera, X, Rodriguez-Fuentes, LJ, Szöke, I, Buzo, A, Metze, F (2014). Query by Example Search on Speech at Mediaeval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 351–352).Joho, H, & Kishida, K (2014). Overview of the NTCIR-11 SpokenQuery&Doc Task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 1–7).NIST. Draft KWS16 Keyword Search Evaluation Plan. https://www.nist.gov/sites/default/files/documents/itl/iad/mig/KWS16-evalplan-v04.pdf . Accessed Feb 2018.Anguera, X, Metze, F, Buzo, A, Szöke, I, Rodriguez-Fuentes, LJ (2013). The spoken web search task. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 921–922).Taras, B, & Nadeu, C (2011). Audio segmentation of broadcast news in the Albayzin-2010 evaluation: overview, results, and discussion. EURASIP Journal on Audio, Speech, and Music Processing, 2011(1), 1–10.Zelenák, M, Schulz, H, Hernando, J (2012). Speaker diarization of broadcast news in Albayzin 2010 evaluation campaign. EURASIP Journal on Audio, Speech, and Music Processing, 2012(19), 1–9.Rodríguez-Fuentes, LJ, Penagarikano, M, Varona, A, Díez, M, Bordel, G (2011). The Albayzin 2010 Language Recognition Evaluation. In Proc. of Interspeech. ISCA, Baixas, (pp. 1529–1532).Tejedor, J, Toledano, DT, Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C, Cardenal, A, Echeverry-Correa, JD, Coucheiro-Limeres, A, Olcoz, J, Miguel, A (2015). Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion. EURASIP, Journal on Audio, Speech and Music Processing, 2015(21), 1–27.Tejedor, J, Toledano, DT, Anguera, X, Varona, A, Hurtado, LF, Miguel, A, Colás, J (2013). Query-by-example spoken term detection ALBAYZIN 2012 evaluation: overview, systems, results, and discussion. EURASIP, Journal on Audio, Speech, and Music Processing, 2013(23), 1–17.Tejedor, J, Toledano, DT, Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations. EURASIP, Journal on Audio, Speech and Music Processing, 2016(1), 1–19.Méndez, F, Docío, L, Arza, M, Campillo, F (2010). The Albayzin 2010 text-to-speech evaluation. In Proc. of FALA. UniversidadeVigo, Vigo, (pp. 317–340).Billa, J, Ma, KW, McDonough, JW, Zavaliagkos, G, Miller, DR, Ross, KN, El-Jaroudi, A (1997). Multilingual speech recognition: the 1996 Byblos Callhome system. In Proc. of Eurospeech. ISCA, Baixas, (pp. 363–366).Killer, M, Stuker, S, Schultz, T (2003). Grapheme based speech recognition. In Proc. of Eurospeech. ISCA, Baixas, (pp. 3141–3144).Burget, L, Schwarz, P, Agarwal, M, Akyazi, P, Feng, K, Ghoshal, A, Glembek, O, Goel, N, Karafiat, M, Povey, D, Rastrow, A, Rose, RC, Thomas, S (2010). Multilingual acoustic modeling for speech recognition based on subspace gaussian mixture models. In Proc. of ICASSP. IEEE, New York, (pp. 4334–4337).Cuayahuitl, H, & Serridge, B (2002). Out-of-vocabulary word modeling and rejection for Spanish keyword spotting systems. In Proc. of MICAI. Springer, Berlin, (pp. 156–165).Tejedor, J (2009). Contributions to keyword spotting and spoken term detection for information retrieval in audio mining. PhD thesis, Universidad Autónoma de Madrid, Madrid, Spain.Tejedor, J, Toledano, DT, Wang, D, King, S, Colás, J (2014). Feature analysis for discriminative confidence estimation in spoken term detection. Computer Speech and Language, 28(5), 1083–1114.Li, J, Wang, X, Xu, B (2014). An empirical study of multilingual and low-resource spoken term detection using deep neural networks. In Proc. of Interspeech. ISCA, Baixas, (pp. 1747–1751).NIST. The Spoken Term Detection (STD) 2006 evaluation plan. http://berlin.csie.ntnu.edu.tw/Courses/Special%20Topics%20in%20Spoken%20Language%20Processing/Lectures2008/SLP2008S-Lecture12-Spoken%20Term%20Detection.pdf . Accessed Feb 2018.Fiscus, JG, Ajot, J, Garofolo, JS, Doddingtion, G (2007). Results of the 2006 spoken term detection evaluation. In Proc. of SSCS. ACM, New York, (pp. 45–50).Martin, A, Doddington, G, Kamm, T, Ordowski, M, Przybocki, M (1997). The DET curve in assessment of detection task performance. In Proc. of Eurospeech. ISCA, Baixas, (pp. 1895–1898).NIST. Evaluation Toolkit (STDEval) software. https://www.nist.gov/itl/iad/mig/tools . Accessed Feb 2018.Union, IT. ITU-T Recommendation P.563: Single-ended method for objective speech quality assessment in narrow-band telephony applications. http://www.itu.int/rec/T-REC-P.563/en . Accessed Feb 2018.Rajput, N, & Metze, F (2011). Spoken web search. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 1–2).Metze, F, Barnard, E, Davel, M, van Heerden, C, Anguera, X, Gravier, G, Rajput, N (2012). The spoken web search task. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 41–42).Szöke, I, Rodriguez-Fuentes, LJ, Buzo, A, Anguera, X, Metze, F, Proenca, J, Lojka, M, Xiong, X (2015). Query by Example Search on Speech at Mediaeval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 81–82).Szöke, I, & Anguera, X (2016). Zero-cost speech recognition task at Mediaeval 2016. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 81–82).Akiba, T, Nishizaki, H, Nanjo, H, Jones, GJF (2014). Overview of the NTCIR-11 spokenquery &doc task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 1–15).Akiba, T, Nishizaki, H, Nanjo, H, Jones, GJF (2016). Overview of the NTCIR-12 spokenquery &doc-2. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 1–13).Schwarz, P (2008). Phoneme recognition based on long temporal context. PhD thesis, FIT, BUT, Brno, Czech Republic.Varona, A, Penagarikano, M, Rodríguez-Fuentes, LJ, Bordel, G (2011). On the use of lattices of time-synchronous cross-decoder phone co-occurrences in a SVM-phonotactic language recognition system. In Proc. of Interspeech. ISCA, Baixas, (pp. 2901–2904).Eyben, F, Wollmer, M, Schuller, B (2010). OpenSMILE—the munich versatile and fast open-source audio feature extractor. In Proc. of ACM Multimedia (MM). ACM, New York, (pp. 1459–1462).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). Finding relevant features for zero-resource query-by-example search on speech. Speech Communication, 84(1), 24–35.Zhang, Y, & Glass, JR (2009). Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams. In Proc. of ASRU. IEEE, New York, (pp. 398–403).Povey, D, Ghoshal, A, Boulianne, G, Burget, L, Glembek, O, Goel, N, Hannemann, M, Motlicek, P, Qian, Y, Schwarz, P, Silovsky, J, Stemmer, G, Vesely, K (2011). The KALDI speech recognition toolkit. In Proc. of ASRU. IEEE, New York, (pp. 1–4).Muller, M. (2007). Information retrieval for music and motion. New York: Springer.Szöke, I, Skacel, M, Burget, L (2014). BUT QUESST 2014 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 621–622).Brümmer, N, & van Leeuwen, D (2006). On calibration of language recognition scores. In Proc of the IEEE Odyssey: The speaker and language recognition workshop. IEEE, New York, (pp. 1–8).Brümmer, N, & de Villiers, E. The BOSARIS toolkit user guide: Theory, algorithms and code for binary classifier score processing. Technical report. https://sites.google.com/site/nikobrummer . Accessed Feb 2018.Meinedo, H, & Neto, J (2005). A stream-based audio segmentation, classification and clustering pre-processing system for broadcast news using ANN models. In Proc. of Interspeech. ISCA, Baixas, (pp. 237–240).Morgan, N, & Bourlard, H (1995). An introduction to hybrid HMM/connectionist continuous speech recognition. IEEE Signal Processing Magazine, 12(3), 25–42.Meinedo, H, Abad, A, Pellegrini, T, Trancoso, I, Neto, J (2010). The L2F broadcast news speech recognition system. In Proc. of FALA. UniversidadeVigo, Vigo, (pp. 93–96).Abad, A, Luque, J, Trancoso, I (2011). Parallel transformation network features for speaker recognition. In Proc. of ICASSP. IEEE, New York, (pp. 5300–5303).Diez, M, Varona, A, Penagarikano, M, Rodriguez-Fuentes, LJ, Bordel, G (2012). On the use of phone log-likelihood ratios as features in spoken language recognition. In Proc. of SLT. IEEE, New York, (pp. 274–279).Diez, M, Varona, A, Penagarikano, M, Rodriguez-Fuentes, LJ, Bordel, G (2014). New insight into the use of phone log-likelihood ratios as features for language recognition. In Proc. of Interspeech. ISCA, Baixas, (pp. 1841–1845).Abad, A, Ribeiro, E, Kepler, F, Astudillo, R, Trancoso, I (2016). Exploiting phone log-likelihood ratio features for the detection of the native language of non-native English speakers. In Proc. of Interspeech. ISCA, Baixas, (pp. 2413–2417).Rodríguez-Fuentes, LJ, Varona, A, Peñagarikano, M, Bordel, G, Díez, M (2014). High-performance query-by-example spoken term detection on the SWS 2013 evaluation. In Proc. of ICASSP. IEEE, New York, (pp. 7819–7823).Vesely, K, Ghoshal, A, Burget, L, Povey, D (2013). Sequence-discriminative training of deep neural networks. In Proc. of Interspeech. ISCA, Baixas, (pp. 2345–2349).Ghahremani, P, BabaAli, B, Povey, D, Riedhammer, K, Trmal, J, Khudanpur, S (2014). A pitch extraction algorithm tuned for automatic speech recognition. In Proc. of ICASSP. IEEE, New York, (pp. 2494–2498).Povey, D, Hannemann, M, Boulianne, G, Burget, L, Ghoshal, A, Janda, M, Karafiat, M, Kombrink, S, Motlicek, P, Qian, Y, Riedhammer, K, Vesely, K, Vu, NT (2012). Generating exact lattices in the WFST framework. In Proc. of ICASSP. IEEE, New York, (pp. 4213–4216).Garcia-Mateo, C, Dieguez-Tirado, J, Docio-Fernandez, L, Cardenal-Lopez, A (2004). Transcrigal: A bilingual system for automatic indexing of broadcast news. In Proc. of LREC. ELRA, Paris, (pp. 2061–2064).Stolcke, A (2002). SRILM—an extensible language modeling toolkit. In Proc. of Interspeech. ISCA, Baixas, (pp. 901–904).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). GTM-UVigo systems for Albayzin 2016 search on speech evaluation. In Proc. of Iberspeech. Springer, Berlin, (pp. 65–74).Chen, G, Khudanpur, S, Povey, D, Trmal, J, Yarowsky, D, Yilmaz, O (2013). Quantifying the value of pronunciation lexicons for keyword search in low resource languages. In Proc. of ICASSP. IEEE, New York, (pp. 8560–8564).Pham, VT, Chen, NF, Sivadas, S, Xu, H, Chen, I-F, Ni, C, Chng, ES, Li, H (2014). System and keyword dependent fusion for spoken term detection. In Proc. of SLT. IEEE, New York, (pp. 430–435).Can, D, & Saraclar, M (2011). Lattice indexing for spoken term detection. IEEE Transactions on Audio, Speech and Language Processing, 19(8), 2338–2347.Miller, DRH, K

Challenges for future food systems: from the Green Revolution to food supply chains with a special focus on sustainability

Finding a food system to feed the growing worldwide population remains a challenge, especially in the current era, where natural resources are being dramatically depleted. From a historical point of view, the Green Revolution, together with biofortification and sustainable intensification, was established as a possible solution to counter hunger and malnutrition during the second half of the 20th century. As a solution, to overcome the limitations attributed to the Green Revolution, food supply chains were developed. The current food system, based on the long food supply chain (LFSC), is characterized by globalization, promoting several advantages for both producers and consumers. However, LFSC has been demonstrated to be unable to feed the global population and, furthermore, it generates negative ecological, environmental, logistical, and nutritional pressures. Thus, novel efficient food systems are required to respond to current environmental and consumers' demands, as is the case of short food supply chain (SFSC). As a recently emerging food system, the evaluation of SFSC sustainability in terms of environmental, economic, and social assessment is yet to be determined. This review is focused on the evolution of food supply systems, starting from the Green Revolution to food supply chains, providing a significant perspective on sustainability.The research leading to these results was supported by MICINN supporting the Ramón y Cajal grant for M. A. Prieto (RYC-2017-22891), the Juan de la Cierva Incorporación for Hui Cao (IJC2020-04605- 5-I) and the FPU grant for A. Soria-Lopez (FPU2020/06140); by Xunta de Galicia for supporting the program (EXCELENCIA-ED431F 2020/12) and by supporting the postdoctoral grant of M. Fraga- Corral (ED481B-2019-096) and the predoctoral grants of M. Carpena (ED481A 2021/313) and of P. Garcia-Oliveira (ED481A-2019/295); and by the European Union through the “NextGenerationEU” program supporting the “Margarita Salas” grant awarded to P. Garcia-Perez. The authors are grateful to Ibero-American Program on Science and Technology (CYTED—AQUA-CIBUS, P317RT0003), to the Bio Based Industries Joint Undertaking (JU) under grant agreement No. 888003 UP4HEALTH Project (H2020-BBI-JTI-2019) that supports the work of P. Otero and P. Garcia-Perez. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Bio Based Industries Consortium. The project SYSTEMIC Knowledge hub on Nutrition and Food Security, has received funding from national research funding parties in Belgium (FWO), France (INRA), Germany (BLE), Italy (MIPAAF), Latvia (IZM), Norway (RCN), Portugal (FCT), and Spain (AEI) in a joint action of JPI HDHL, JPI-OCEANS and FACCE-JPI launched in 2019 under the ERA-NET ERA-HDHL (No. 696295)

Colour and stability of the six common anthocyanidin 3-glucosides

This study on anthocyanin stability and colour variation (lambda max, absorptivity) in the pH range 1-12 during a period of 60 days storage at 10 and 23ºC, was conducted on the 3-glucosides of the six common anthocyanidins. It was mostly in the alkaline region that differences in colour and stability became significant. Although it has been generally accepted that anthocyanins are stable only at low pH values, this study revealed that, for some of the anthocyanin 3-glucosides (e.g. malvidin 3-glucoside), the bluish colours were rather intense and stability relatively high in the alkaline region. Thus, they can be regarded as potential colorants for some slightly alkaline food products.Fundação para a Ciência e Tecnologi

Albayzín-2014 evaluation: audio segmentation and classification in broadcast news domains

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13636-015-0076-3Audio segmentation is important as a pre-processing task to improve the performance of many speech technology tasks and, therefore, it has an undoubted research interest. This paper describes the database, the metric, the systems and the results for the Albayzín-2014 audio segmentation campaign. In contrast to previous evaluations where the task was the segmentation of non-overlapping classes, Albayzín-2014 evaluation proposes the delimitation of the presence of speech, music and/or noise that can be found simultaneously. The database used in the evaluation was created by fusing different media and noises in order to increase the difficulty of the task. Seven segmentation systems from four different research groups were evaluated and combined. Their experimental results were analyzed and compared with the aim of providing a benchmark and showing up the promising directions in this field.This work has been partially funded by the Spanish Government and the European Union (FEDER) under the project TIN2011-28169-C05-02 and supported by the European Regional Development Fund and the Spanish Government (‘SpeechTech4All Project’ TEC2012-38939-C03

Benefits and drawbacks of ultrasound-assisted extraction for the recovery of bioactive compounds from marine algae

The increase in life expectancy has led to the appearance of chronic diseases and interest in healthy aging, in turn promoting a growing interest in bioactive compounds (BCs) and functional ingredients. There are certain foods or products rich in functional ingredients, and algae are one of them. Algae consumption has been nominal in Europe until now. However, in recent years, it has grown significantly, partly due to globalization and the adoption of new food trends. With the aim of obtaining BCs from foods, multiple methods have been proposed, ranging from conventional ones, such as maceration or Soxhlet extraction, to more innovative methods, e.g., ultrasound-assisted extraction (UAE). UAE constitutes a novel method, belonging to so-called green chemistry, that enables the extraction of BCs requiring lower amounts of solvent and energy costs, preserving the integrity of such molecules. In recent years, this method has been often used for the extraction of different BCs from a wide range of algae, especially polysaccharides, such as carrageenans and alginate; pigments, including fucoxanthin, chlorophylls, or -carotene; and phenolic compounds, among others. In this way, the application of UAE to marine algae is an efficient and sustainable strategy to pursue their deep characterization as a new source of BCs, especially suitable for vegetarian and vegan diets.The research leading to these results was supported by MICINN supporting the Ramón y Cajal grant for M.A. Prieto (RYC-2017-22891) and the FPU grant for A. Carreira-Casais (FPU2016/06135); by Xunta de Galicia for supporting the pre-doctoral grants of P. Garcia-Oliveira (ED481A-2019/295) and A.G. Pereira (ED481A-2019/0228); by University of Vigo for supporting the predoctoral grant of M. Carpena (Uvigo-00VI 131H 6410211) and by Becas de Fundación ONCE Programme “Oportunidad al Talento” that supports the work of A. Soria-Lopez.info:eu-repo/semantics/publishedVersio

Single-cell proteins obtained by circular economy intended as a feed ingredient in aquaculture

The constant increment in the world’s population leads to a parallel increase in the demand for food. This situation gives place the need for urgent development of alternative and sustainable resources to satisfy this nutritional requirement. Human nutrition is currently based on fisheries, which accounts for 50% of the fish production for human consumption, but also on agriculture, livestock, and aquaculture. Among them, aquaculture has been pointed out as a promising source of animal protein that can provide the population with high-quality protein food. This productive model has also gained attention due to its fast development. However, several aquaculture species require considerable amounts of fish protein to reach optimal growth rates, which represents its main drawback. Aquaculture needs to become sustainable using renewable source of nutrients with high contents of proteins to ensure properly fed animals. To achieve this goal, different approaches have been considered. In this sense, single-cell protein (SCP) products are a promising solution to replace fish protein from fishmeal. SCP flours based on microbes or algae biomass can be sustainably obtained. These microorganisms can be cultured by using residues supplied by other industries such as agriculture, food, or urban areas. Hence, the application of SCP for developing innovative fish meal offers a double solution by reducing the management of residues and by providing a sustainable source of proteins to aquaculture. However, the use of SCP as aquaculture feed also has some limitations, such as problems of digestibility, presence of toxins, or difficulty to scale-up the production process. In this work, we review the potential sources of SCP, their respective production processes, and their implementation in circular economy strategies, through the revalorization and exploitation of different residues for aquaculture feeding purposes. The data analyzed show the positive effects of SCP inclusion in diets and point to SCP meals as a sustainable feed system. However, new processes need to be exploited to improve yield. In that direction, the circular economy is a potential alternative to produce SCP at any time of the year and from various cost-free substrates, almost without a negative impact.Bio Based Industries Joint Undertaking | Ref. H2020-BBI-JTI-2019Ministerio de Ciencia e Innovación | Ref. RYC-2017-22891Ministerio de Ciencia e Innovación | Ref. RYC-2020-030365-IMinisterio de Ciencia e Innovación | Ref. IJC2020-046055-IMinisterio de Ciencia e Innovación | Ref. FPU2020/06140Xunta de Galicia | Ref. ED481B-2019/096Xunta de Galicia | Ref. ED481B-2021/152Xunta de Galicia | Ref. ED481A-2019/295Xunta de Galicia | Ref. ED481A-2019/0228CYTED—AQUA-CIBUS | Ref. P317RT0003European Commission | Ref. H2020-ERA-NET ERA-HDHL n. 69629

Seaweed polysaccharides: emerging extraction technologies, chemical modifications and bioactive properties

Nowadays, consumers are increasingly aware of the relationship between diet and health, showing a greater preference of products from natural origin. In the last decade, seaweeds have outlined as one of the natural sources with more potential to obtain bioactive carbohydrates. Numerous seaweed polysaccharides have aroused the interest of the scientific community, due to their biological activities and their high potential on biomedical, functional food and technological applications. To obtain polysaccharides from seaweeds, it is necessary to find methodologies that improve both yield and quality and that they are profitable. Nowadays, environmentally friendly extraction technologies are a viable alternative to conventional methods for obtaining these products, providing several advantages like reduced number of solvents, energy and time. On the other hand, chemical modification of their structure is a useful approach to improve their solubility and biological properties, and thus enhance the extent of their potential applications since some uses of polysaccharides are still limited. The present review aimed to compile current information about the most relevant seaweed polysaccharides, available extraction and modification methods, as well as a summary of their biological activities, to evaluate knowledge gaps and future trends for the industrial applications of these compounds. Key teaching points: Structure and biological functions of main seaweed polysaccharides. Emerging extraction methods for sulfate polysaccharides. Chemical modification of seaweeds polysaccharides. Potential industrial applications of seaweed polysaccharides. Biological activities, knowledge gaps and future trends of seaweed polysaccharides.The research leading to these results was supported by MICINN supporting the Ramón y Cajal grant for M.A. Prieto (RYC-2017-22891); by Xunta de Galicia for supporting the program EXCELENCIA-ED431F 2020/12, the post-doctoral grant of M. Fraga-Corral (ED481B-2019/096), the pre-doctoral grant of P. Garcia-Oliveira (ED481A-2019/295) the program Grupos de Referencia Competitiva (GRUPO AA1-GRC 2018) that supports the work of J. Echave; by University of Vigo for supporting the predoctoral grant of M. Carpena (Uvigo-00VI 131H 6410211) and Becas de Fundación ONCE Programme “Oportunidad al Talento” to support the work of A. Soria-Lopez. Authors are grateful to Ibero-American Program on Science and Technology (CYTED— AQUA-CIBUS, P317RT0003), to the Bio Based Industries Joint Undertaking (JU) under grant agreement No 888003 UP4HEALTH Project (H2020-BBI-JTI-2019) that supports the work of P. Otero. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Bio Based Industries Consortium. The project SYSTEMIC Knowledge hub on Nutrition and Food Security, has received funding from national research funding parties in Belgium (FWO), France (INRA), Germany (BLE), Italy (MIPAAF), Latvia (IZM), Norway (RCN), Portugal (FCT), and Spain (AEI) in a joint action of JPI HDHL, JPI-OCEANS and FACCE-JPI launched in 2019 under the ERA-NET ERA-HDHL (n° 696295).info:eu-repo/semantics/publishedVersio

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

Isatuximab in combination with lenalidomide and dexamethasone in patients with high-risk smoldering multiple myeloma: Updated safety run-in results from the randomized phase 3 ithaca study

Background: Results from a randomized, Phase 3 study by the Spanish Myeloma Group (PETHEMA/GEM) previously showed that treatment with lenalidomide plus dexamethasone (Rd) may delay progression to active disease in patients (pts) with high-risk smoldering multiple myeloma (SMM), compared with observation. To further improve outcomes, addition of the anti-CD38 antibody isatuximab (Isa) to lenalidomide and dexamethasone (Isa-Rd) for the treatment of pts with high-risk SMM is being evaluated in the ongoing, randomized, multi-center, Phase 3 ITHACA study (NCT04270409). Initial findings from the safety run-in analysis of this trial have shown a manageable safety profile and encouraging, preliminary anti-myeloma activity. We now report updated safety and efficacy results from the safety run-in part of ITHACA at a median follow-up of 19.4 months. Methods: Pts were included in the study if they had been diagnosed within 5 years with SMM (per the International Myeloma Working Group [IMWG] criteria) and had high-risk SMM according to the Mayo '20-2-20' and/or updated PETHEMA model criteria. Pts who had received prior anti-myeloma treatment were not eligible. Enrolled pts received Isa 10 mg/kg IV on day (D) 1, 8, 15, and 22 in cycle (C) 1, D1 and D15 C2-12, D1 C13-36; plus R D1-21 (25 mg C1-9; 10 mg C10-24) and d weekly (40 mg, 20 mg for ≥75 yr-old pts C1-9; 20 mg C10-24). Cycle duration was 28 days. Safety evaluations included treatment-emergent AEs (TEAEs)/serious AEs and laboratory parameters, graded by NCI-CTCAE v5.0. Response was determined by IMWG criteria (2016). Mandatory imaging by MRI and/or low-dose whole-body CT/PET-CT, and assessments of minimal residual disease (MRD, by next-generation sequencing in pts with very good partial response [VGPR] or better), were performed at protocol-defined time points. The primary study objective for the safety run-in was to confirm the recommended dose of Isa in combination with Rd. Overall response rate (ORR) and MRD negativity rate at 10-5 sensitivity were included as secondary endpoints.Sanof