Combining Several ASR Outputs in a Graph-Based SLU System

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25751-8_66In this paper, we present an approach to Spoken Language Understanding (SLU) where we perform a combination of multiple hypotheses from several Automatic Speech Recognizers (ASRs) in order to reduce the impact of recognition errors in the SLU module. This combination is performed using a Grammatical Inference algorithm that provides a generalization of the input sentences by means of a weighted graph of words. We have also developed a specific SLU algorithm that is able to process these graphs of words according to a stochastic semantic modelling.The results show that the combinations of several hypotheses from the ASR module outperform the results obtained by taking just the 1-best transcriptionThis work is partially supported by the Spanish MEC under contract TIN2014-54288-C4-3-R and FPU Grant AP2010-4193.Calvo Lance, M.; Hurtado Oliver, LF.; García-Granada, F.; Sanchís Arnal, E. (2015). Combining Several ASR Outputs in a Graph-Based SLU System. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer. 551-558. https://doi.org/10.1007/978-3-319-25751-8_66S551558Bangalore, S., Bordel, G., Riccardi, G.: Computing consensus translation from multiple machine translation systems. In: ASRU, pp. 351–354 (2001)Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., de Letona, I.L., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: LREC, pp. 1636–1639 (2006)Bonneau-Maynard, H., Lefèvre, F.: Investigating stochastic speech understanding. In: IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 260–263 (2001)Calvo, M., García, F., Hurtado, L.F., Jiménez, S., Sanchis, E.: Exploiting multiple hypotheses for multilingual spoken language understanding. In: CoNLL, pp. 193–201 (2013)Fiscus, J.G.: A post-processing system to yield reduced word error rates: recognizer output voting error reduction (ROVER). In: 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 347–354 (1997)Hahn, S., Dinarelli, M., Raymond, C., Lefèvre, F., Lehnen, P., De Mori, R., Moschitti, A., Ney, H., Riccardi, G.: Comparing stochastic approaches to spoken language understanding in multiple languages. IEEE Transactions on Audio, Speech, and Language Processing 6(99), 1569–1583 (2010)Hakkani-Tür, D., Béchet, F., Riccardi, G., Tür, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language 20(4), 495–514 (2006)He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006)Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., Higgins, D.G.: ClustalW and ClustalX version 2.0. Bioinformatics 23(21), 2947–2948 (2007)Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. IJPRAI 16(3), 301–307 (2002)Tür, G., Deoras, A., Hakkani-Tür, D.: Semantic parsing using word confusion networks with conditional random fields. In: INTERSPEECH (2013

Using word graphs as intermediate representation of uttered sentences

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33275-3_35We present an algorithm for building graphs of words as an intermediate representation of uttered sentences. No language model is used. The input data for the algorithm are the pronunciation lexicon organized as a tree and the sequence of acoustic frames. The transition between consecutive units are considered as additional units. Nodes represent discrete instants of time, arcs are labelled with words, and a confidence measure is assigned to each detected word, which is computed by using the phonetic probabilities of the subsequence of acoustic frames used for completing the word. We evaluated the obtained word graphs by searching the path that best matches with the correct sentence and then measuring the word accuracy, i.e. the oracle word accuracy. © 2012 Springer-Verlag.This work was supported by the Spanish MICINN under contract TIN2011-28169-C05-01 and the Vic. d’Investigació of the UPV under contract 20110897.Gómez Adrian, JA.; Sanchís Arnal, E. (2012). Using word graphs as intermediate representation of uttered sentences. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 284-291. doi:10.1007/978-3-642-33275-3_35S284291Ortmanns, S., Ney, H., Aubert, X.: A word graph algorithm for large vocabulary continuous speech recognition. Computer Speech and Language 11, 43–72 (1997)Ney, H., Ortmanns, S., Lindam, I.: Extensions to the word graph method for large vocabulary continuous speech recognition. In: Proceedings of IEEE ICASSP 1997, Munich, Germany, vol. 3, pp. 1791–1794 (1997)Wessel, F., Schlüter, R., Macherey, K., Ney, H.: Confidence Measures for Large Vocabulary Continuous Speech Recognition. IEEE Transactions on Speech and Audio Processing 9(3), 288–298 (2001)Ferreiros, J., San-Segundo, R., Fernández, F., D’Haro, L.-F., Sama, V., Barra, R., Mellén, P.: New word-level and sentence-level confidence scoring using graph theory calculus and its evaluation on speech understanding. In: Proceedings of INTERSPEECH 2005, Lisbon, Portugal, pp. 3377–3380 (2005)Raymond, C., Béchet, F., De Mori, R., Damnati, G.: On the use of finite state transducers for semantic interpretation. Speech Communication 48, 288–304 (2006)Hakkani-Tür, D., Béchet, F., Riccardi, G., Tur, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech and Language 20, 495–514 (2006)Justo, R., Pérez, A., Torres, M.I.: Impact of the Approaches Involved on Word-Graph Derivation from the ASR System. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 668–675. Springer, Heidelberg (2011)Gómez, J.A., Calvo, M.: Improvements on Automatic Speech Segmentation at the Phonetic Level. In: San Martin, C., Kim, S.-W. (eds.) CIARP 2011. LNCS, vol. 7042, pp. 557–564. Springer, Heidelberg (2011)Calvo, M., Gómez, J.A., Sanchis, E., Hurtado, L.F.: An algorithm for automatic speech understanding over word graphs. Procesamiento del Lenguaje Natural (48) (accepted, pending of publication, 2012)Moreno, A., Poch, D., Bonafonte, A., Lleida, E., Llisterri, J., Mariño, J.B., Nadeu, C.: Albayzin Speech Database: Design of the Phonetic Corpus. In: Proceedings of Eurospeech, Berlin, Germany, vol. 1, pp. 653–656 (September 1993)Benedí, J.M., Lleida, E., Varona, A., Castro, M., Galiano, I., Justo, R., López, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: Proc. of LREC 2006, Genova, Italy (2006

A multilingual SLU system based on semantic decoding of graphs of words

In this paper, we present a statistical approach to Language Understanding that allows to avoid the effort of obtaining new semantic models when changing the language. This way, it is not necessary to acquire and label new training corpora in the new language. Our approach consists of learning all the semantic models in a target language and to do the semantic decoding of the sentences pronounced in the source language after a translation process. In order to deal with the errors and the lack of coverage of the translations, a mechanism to generalize the result of several translators is proposed. The graph of words generated in this phase is the input to the semantic decoding algorithm specifically designed to combine statistical models and graphs of words. Some experiments that show the good behavior of the proposed approach are also presented.Calvo Lance, M.; Hurtado Oliver, LF.; García Granada, F.; Sanchís Arnal, E. (2012). A multilingual SLU system based on semantic decoding of graphs of words. En Advances in Speech and Language Technologies for Iberian Languages. Springer Verlag (Germany). 328:158-167. doi:10.1007/978-3-642-35292-8_17S158167328Hahn, S., Dinarelli, M., Raymond, C., Lefèvre, F., Lehnen, P., De Mori, R., Moschitti, A., Ney, H., Riccardi, G.: Comparing stochastic approaches to spoken language understanding in multiple languages. IEEE Transactions on Audio, Speech, and Language Processing 6(99), 1569–1583 (2010)Raymond, C., Riccardi, G.: Generative and discriminative algorithms for spoken language understanding. In: Proceedings of Interspeech 2007, pp. 1605–1608 (2007)Tur, G., Mori, R.D.: Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, 1st edn. Wiley (2011)Maynard, H.B., Lefèvre, F.: Investigating Stochastic Speech Understanding. In: Proc. of IEEE Automatic Speech Recognition and Understanding Workshop, ASRU (2001)Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. IJPRAI 16(3), 301–307 (2002)He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006)De Mori, R., Bechet, F., Hakkani-Tur, D., McTear, M., Riccardi, G., Tur, G.: Spoken language understanding: A survey. IEEE Signal Processing Magazine 25(3), 50–58 (2008)Hakkani-Tür, D., Béchet, F., Riccardi, G., Tur, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language 20(4), 495–514 (2006)Tur, G., Wright, J., Gorin, A., Riccardi, G., Hakkani-Tür, D.: Improving spoken language understanding using word confusion networks. In: Proceedings of the ICSLP. Citeseer (2002)Tur, G., Hakkani-Tür, D., Schapire, R.E.: Combining active and semi-supervised learning for spoken language understanding. Speech Communication 45, 171–186 (2005)Ortega, L., Galiano, I., Hurtado, L.F., Sanchis, E., Segarra, E.: A statistical segment-based approach for spoken language understanding. In: Proc. of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 1836–1839 (2010)Sim, K.C., Byrne, W.J., Gales, M.J.F., Sahbi, H., Woodland, P.C.: Consensus network decoding for statistical machine translation system combination. In: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (2007)Bangalore, S., Bordel, G., Riccardi, G.: Computing Consensus Translation from Multiple Machine Translation Systems. In: Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2001, pp. 351–354 (2001)Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., Higgins, D.G.: ClustalW and ClustalX version 2.0. Bioinformatics 23(21), 2947–2948 (2007)Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López de Letona, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: Proceedings of LREC 2006, Genoa, Italy, pp. 1636–1639 (May 2006

Interactive handwriting recognition with limited user effort

The final publication is available at Springer via http://dx.doi.org/10.1007/s10032-013-0204-5[EN] Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. Although post-editing automatic recognition of handwritten text is feasible, it is not clearly better than simply ignoring it and transcribing the document from scratch. A more effective approach is to follow an interactive approach in which both the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Nevertheless, in some applications, the user effort available to transcribe documents is limited and fully supervision of the system output is not realistic. To circumvent these problems, we propose a novel interactive approach which efficiently employs user effort to transcribe a document by improving three different aspects. Firstly, the system employs a limited amount of effort to solely supervise recognised words that are likely to be incorrect. Thus, user effort is efficiently focused on the supervision of words for which the system is not confident enough. Secondly, it refines the initial transcription provided to the user by recomputing it constrained to user supervisions. In this way, incorrect words in unsupervised parts can be automatically amended without user supervision. Finally, it improves the underlying system models by retraining the system from partially supervised transcriptions. In order to prove these statements, empirical results are presented on two real databases showing that the proposed approach can notably reduce user effort in the transcription of handwritten text in (old) documents.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No 287755 (transLectures). Also supported by the Spanish Government (MICINN, MITyC, "Plan E", under Grants MIPRCV "Consolider Ingenio 2010", MITTRAL (TIN2009-14633-C03-01), erudito.com (TSI-020110-2009-439), iTrans2 (TIN2009-14511), and FPU (AP2007-02867), and the Generalitat Valenciana (Grants Prometeo/2009/014 and GV/2010/067).Serrano Martinez Santos, N.; Giménez Pastor, A.; Civera Saiz, J.; Sanchis Navarro, JA.; Juan Císcar, A. (2014). Interactive handwriting recognition with limited user effort. International Journal on Document Analysis and Recognition. 17(1):47-59. https://doi.org/10.1007/s10032-013-0204-5S4759171Agua, M., Serrano, N., Civera, J., Juan, A.: Character-based handwritten text recognition of multilingual documents. In: Proceedings of Advances in Speech and Language Technologies for Iberian Languages (IBERSPEECH 2012), Madrid (Spain), pp. 187–196 (2012)Ahn, L.V., Maurer, B., Mcmillen, C., Abraham, D., Blum, M.: reCAPTCHA: human-based character recognition via web security measures. 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The CALO meeting speech recognition and understanding system

ABSTRACT The CALO Meeting Assistant provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multi-party meetings, and is part of the larger CALO personal assistant system. This paper summarizes the CALO-MA architecture and its speech recognition and understanding components, which include realtime and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, and summarization

Automatic summarization of voicemail messages using lexical and prosodic features

This article presents trainable methods for extracting principal content words from voicemail messages. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words with each word described by a vector of lexical and prosodic features. We use an ROC-based algorithm, Parcel, to select input features (and classifiers). We have performed a series of objective and subjective evaluations using unseen data from two different speech recognition systems as well as human transcriptions of voicemail speech

Spoken language understanding and interaction: machine learning for human-like conversational systems

In recent years, the interest in research in speech understanding and spoken interaction has soared due to the emergence of virtual personal assistants. However, while the ability of these agents to recognise conversational speech is maturing rapidly, their ability to understand and interact is still limited. At the same time we have witnessed the development of the number of models based on machine learning that made a huge impact on spoken language understanding accuracies and the interaction quality overall. This special issue brings together a number of articles that tackle different aspects of spoken language understanding and interaction: clarifications in dialogues, adaptation to different domains, semantic tagging and error handling. These studies all have a common purpose of building human-like conversational systems

Role recognition for meeting participants: an approach based on lexical information and social network analysis

This paper presents experiments on the automatic recognition of roles in meetings. The proposed approach combines two sources of information: the lexical choices made by people playing different roles on one hand, and the Social Networks describing the interactions between the meeting participants on the other hand. Both sources lead to role recognition results significantly higher than chance when used separately, but the best results are obtained with their combination. Preliminary experiments obtained over a corpus of 138 meeting recordings (over 45 hours of material) show that around 70% of the time is labeled correctly in terms of role

Towards scalable dataset construction: An active learning approach

Abstract. As computer vision research considers more object categories and greater variation within object categories, it is clear that larger and more exhaustive datasets are necessary. However, the process of collecting such datasets is laborious and monotonous. We consider the setting in which many images have been automatically collected for a visual category (typically by automatic internet search), and we must separate relevant images from noise. We present a discriminative learning process which employs active, online learning to quickly classify many images with minimal user input. The principle advantage of this work over previous endeavors is its scalability. We demonstrate precision which is often superior to the state-of-the-art, with scalability which exceeds previous work.