Real-Time Statistical Speech Translation

This research investigates the Statistical Machine Translation approaches to translate speech in real time automatically. Such systems can be used in a pipeline with speech recognition and synthesis software in order to produce a real-time voice communication system between foreigners. We obtained three main data sets from spoken proceedings that represent three different types of human speech. TED, Europarl, and OPUS parallel text corpora were used as the basis for training of language models, for developmental tuning and testing of the translation system. We also conducted experiments involving part of speech tagging, compound splitting, linear language model interpolation, TrueCasing and morphosyntactic analysis. We evaluated the effects of variety of data preparations on the translation results using the BLEU, NIST, METEOR and TER metrics and tried to give answer which metric is most suitable for PL-EN language pair.Comment: machine translation, polish englis

Statistical speech translation system based on voice recognition optimization using multimodal sources of knowledge and characteristics vectors

Synergic combination of different sources of knowledge is a key issue for the development of modern statistical translators. In this work, a speech translation statistical system that adds additional other-than-voice information in a voice translation system is presented. The additional information serves as a base for the log-linear combination of several statistical models. We describe the theoretical framework of the problem, summarize the overall architecture of the system, and show how the system is enhanced with the additional information. Our real prototype implements a real-time speech translation system from Spanish to English that is adapted to specific teaching-related environments.This work has been partially supported by the Generalitat Valenciana and the Universidad Politecnica de Valencia.Canovas Solbes, A.; Tomás Gironés, J.; Lloret, J.; García Pineda, M. (2013). Statistical speech translation system based on voice recognition optimization using multimodal sources of knowledge and characteristics vectors. Computer Standards and Interfaces. 35(5):490-506. doi:10.1016/j.csi.2012.09.003S49050635

Speech Translation Statistical System for Teaching Environments and Conference Speeches

The synergic combination of different sources of knowledge is a key aspect in the development of modern statistical translators. The effect and implications of adding additional other-than-voice information in a voice translation system for teaching environments and conference speakers is described in this work. The additional information serves as the bases for the log-linear combination of several statistical models. A prototype that implements a real-time speech translation system from Spanish to English is presented. In the scenario of analysis a teacher, or presenter, as speaker giving its presentation could use a real time translation system for foreign students or participants. The speaker could add slides or class notes as additional reference to the voice translation system. Should notes be already translated into the destination language the system could have even more accuracy. In this paper, first, we present the theoretical framework of the problem, then, we summarize the overall architecture of the system, next, we specify the speech recognition module and the machine translation module, then, we show how the system is enhanced with capabilities related to capturing the additional information, and, finally, we present the performance results of the developed system.Tomás Gironés, J.; Canovas Solbes, A.; Lloret, J.; García Pineda, M. (2011). Speech Translation Statistical System for Teaching Environments and Conference Speeches. International Journal on Advances in Intelligent Systems. 4(1):20-30. http://hdl.handle.net/10251/46962S20304

Discriminative ridge regression algorithm for adaptation in statistical machine translation

[EN] We present a simple and reliable method for estimating the log-linear weights of a state-of-the-art machine translation system, which takes advantage of the method known as discriminative ridge regression (DRR). Since inappropriate weight estimations lead to a wide variability of translation quality results, reaching a reliable estimate for such weights is critical for machine translation research. For this reason, a variety of methods have been proposed to reach reasonable estimates. In this paper, we present an algorithmic description and empirical results proving that DRR is able to provide comparable translation quality when compared to state-of-the-art estimation methods [i.e. MERT and MIRA], with a reduction in computational cost. Moreover, the empirical results reported are coherent across different corpora and language pairs.The research leading to these results were partially supported by projects CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER) and PROMETEO/2018/004. We also acknowledge NVIDIA for the donation of a GPU used in this work.Chinea-Ríos, M.; Sanchis-Trilles, G.; Casacuberta Nolla, F. (2019). Discriminative ridge regression algorithm for adaptation in statistical machine translation. Pattern Analysis and Applications. 22(4):1293-1305. https://doi.org/10.1007/s10044-018-0720-5S12931305224Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda A, Ney H, Tomás J, Vidal E et al (2009) Statistical approaches to computer-assisted translation. Comput Ling 35(1):3–28Bojar O, Buck C, Federmann C, Haddow B, Koehn P, Monz C, Post M, Specia L (eds) (2014) Proceedings of the ninth workshop on statistical machine translation. Association for Computational LinguisticsBrown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Ling 19:263–311Callison-Burch C, Koehn P, Monz C, Peterson K, Przybocki M, Zaidan OF (2010) Findings of the 2010 joint workshop on statistical machine translation and metrics for machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 17–53Chen B, Cherry C (2014) A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the workshop on statistical machine translation, pp 362–367Cherry C, Foster G (2012) Batch tuning strategies for statistical machine translation. In: Proceedings of the North American chapter of the association for computational linguistics, pp 427–436Clark JH, Dyer C, Lavie A, Smith NA (2011) Better hypothesis testing for statistical machine translation: controlling for optimizer instability. In: Proceedings of the annual meeting of the association for computational linguistics, pp 176–181Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585Hasler E, Haddow B, Koehn P (2011) Margin infused relaxed algorithm for moses. Prague Bull Math Ling 96:69–78Hopkins M, May J (2011) Tuning as ranking. In: Proceedings of the conference on empirical methods in natural language processing, pp 1352–1362Kneser R, Ney H (1995) Improved backing-off for m-gram language modeling. In: Proceedings of the international conference on acoustics, speech and signal processing, pp 181–184Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the machine translation summit, pp 79–86Koehn P (2010) Statistical machine translation. Cambridge University Press, CambridgeKoehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 177–180Lavie MDA (2014) Meteor universal: language specific translation evaluation for any target language. In: Proceedings of the annual meeting of the association for computational linguistics, pp 376–387Marie B, Max A (2015) Multi-pass decoding with complex feature guidance for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 554–559Martínez-Gómez P, Sanchis-Trilles G, Casacuberta F (2012) Online adaptation strategies for statistical machine translation in post-editing scenarios. Pattern Recogn 45(9):3193–3203Nakov P, Vogel S (2017) Robust tuning datasets for statistical machine translation. arXiv:1710.00346Neubig G, Watanabe T (2016) Optimization for statistical machine translation: a survey. Comput Ling 42(1):1–54Och FJ (2003) Minimum error rate training in statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 160–167Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Ling 29:19–51Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the international conference on acoustics, speech and signal processing, pp 311–318Sanchis-Trilles G, Casacuberta F (2010) Log-linear weight optimisation via Bayesian adaptation in statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 1077–1085Sanchis-Trilles G, Casacuberta F (2015) Improving translation quality stability using Bayesian predictive adaptation. Comput Speech Lang 34(1):1–17Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the annual meeting of the association for machine translation in the Americas, pp 223–231Sokolov A, Yvon F (2011) Minimum error rate training semiring. In: Proceedings of the annual conference of the European association for machine translation, pp 241–248Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. Pattern Anal Mach Intell 22(8):747–757Stolcke A (2002) Srilm—an extensible language modeling toolkit. In: Proceedings of the international conference on spoken language processing, pp 901–904Tiedemann J (2009) News from opus—a collection of multilingual parallel corpora with tools and interfaces. In: Proceedings of the recent advances in natural language processing, pp 237–248Tiedemann J (2012) Parallel data, tools and interfaces in opus. In: Proceedings of the language resources and evaluation conference, pp 2214–221

Advanced Speech Communication System for Deaf People

This paper describes the development of an Advanced Speech Communication System for Deaf People and its field evaluation in a real application domain: the renewal of Driver’s License. The system is composed of two modules. The first one is a Spanish into Spanish Sign Language (LSE: Lengua de Signos Española) translation module made up of a speech recognizer, a natural language translator (for converting a word sequence into a sequence of signs), and a 3D avatar animation module (for playing back the signs). The second module is a Spoken Spanish generator from sign writing composed of a visual interface (for specifying a sequence of signs), a language translator (for generating the sequence of words in Spanish), and finally, a text to speech converter. For language translation, the system integrates three technologies: an example based strategy, a rule based translation method and a statistical translator. This paper also includes a detailed description of the evaluation carried out in the Local Traffic Office in the city of Toledo (Spain) involving real government employees and deaf people. This evaluation includes objective measurements from the system and subjective information from questionnaire

Passive-aggressive for on-line learning in statistical machine translation

New variations on the application of the passive-aggressive algorithm to statistical machine translation are developed and compared to previously existing approaches. In online adaptation, the system needs to adapt to real-world changing scenarios, where training and tuning only take place when the system is set-up for the first time. Post-edit information, as described by a given quality measure, is used as valuable feedback within the passive-aggressive framework, adapting the statistical models on-line. First, by modifying the translation model parameters, and alternatively, by adapting the scaling factors present in stateof- the-art SMT systems. Experimental results show improvements in translation quality by allowing the system to learn on a sentence-by-sentence basis.This paper is based upon work supported by the EC (FEDER/FSE) and the Spanish MICINN under projects MIPRCV “Consolider Ingenio 2010” (CSD2007-00018) and iTrans2 (TIN2009-14511). Also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project, by the Generalitat Valenciana under grant Prometeo/2009/014 and scholarship GV/2010/067 and by the UPV under grant 20091027.Martínez Gómez, P.; Sanchis Trilles, G.; Casacuberta Nolla, F. (2011). Passive-aggressive for on-line learning in statistical machine translation. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 6669:240-247. https://doi.org/10.1007/978-3-642-21257-4_30S2402476669Barrachina, S., et al.: Statistical approaches to computer-assisted translation. Computational Linguistics 35(1), 3–28 (2009)Callison-Burch, C., Bannard, C., Schroeder, J.: Improving statistical translation through editing. In: Proc. of 9th EAMT Workshop Broadening Horizons of Machine Translation and its Applications, Malta (April 2004)Callison-Burch, C., Fordyce, C., Koehn, P., Monz, C., Schroeder, J.: (meta-) evaluation of machine translation. In: Proc. of the Workshop on SMT, pp. 136–158. ACL (June 2007)Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. Journal of Machine Learning Research 7, 551–585 (2006)Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing II, pp. 181–184 (May 1995)Koehn, P.: Europarl: A parallel corpus for statistical machine translation. In: Proc. of the MT Summit X, pp. 79–86 (2005)Koehn, P., et al.: Moses: Open source toolkit for statistical machine translation. In: Proc. of the ACL Demo and Poster Sessions, Prague, Czech Republic, pp. 177–180 (2007)Och, F., Ney, H.: Discriminative training and maximum entropy models for statistical machine translation. In: Proc. of the ACL 2002, pp. 295–302 (2002)Och, F.: Minimum error rate training for statistical machine translation. In: Dignum, F.P.M. (ed.) ACL 2003. LNCS (LNAI), vol. 2922, pp. 160–167. Springer, Heidelberg (2004)Ortiz-Martínez, D., García-Varea, I., Casacuberta, F.: Online learning for interactive statistical machine translation. In: Proceedings of NAACL HLT, Los Angeles (June 2010)Papineni, K., Roukos, S., Ward, T.: Maximum likelihood and discriminative training of direct translation models. In: Proc. of ICASSP 1998, pp. 189–192 (1998)Papineni, K., Roukos, S., Ward, T., Zhu, W.: Bleu: A method for automatic evaluation of machine translation. In: Proc. of ACL 2002, pp. 311–318 (2002)Reverberi, G., Szedmak, S., Cesa-Bianchi, N., et al.: Deliverable of package 4: Online learning algorithms for computer-assisted translation (2008)Sanchis-Trilles, G., Casacuberta, F.: Log-linear weight optimisation via bayesian adaptation in statistical machine translation. In: Proc. of COLING 2010, Beijing, China, pp. 1077–1085 (August 2010)Snover, M., et al.: A study of translation edit rate with targeted human annotation. In: Proc. of AMTA 2006, Cambridge, Massachusetts, USA, pp. 223–231 (August 2006)Zens, R., Och, F., Ney, H.: Phrase-based statistical machine translation. In: Jarke, M., Koehler, J., Lakemeyer, G. (eds.) KI 2002. LNCS (LNAI), vol. 2479, pp. 18–32. Springer, Heidelberg (2002

Incorporating source-language paraphrases into phrase-based SMT with confusion networks

To increase the model coverage, sourcelanguage paraphrases have been utilized to boost SMT system performance. Previous work showed that word lattices constructed from paraphrases are able to reduce out-ofvocabulary words and to express inputs in different ways for better translation quality. However, such a word-lattice-based method suffers from two problems: 1) path duplications in word lattices decrease the capacities for potential paraphrases; 2) lattice decoding in SMT dramatically increases the search space and results in poor time efficiency. Therefore, in this paper, we adopt word confusion networks as the input structure to carry source-language paraphrase information. Similar to previous work, we use word lattices to build word confusion networks for merging of duplicated paths and faster decoding. Experiments are carried out on small-, medium- and large-scale English– Chinese translation tasks, and we show that compared with the word-lattice-based method, the decoding time on three tasks is reduced significantly (up to 79%) while comparable translation quality is obtained on the largescale task