An active learning approach for statistical spoken language understanding

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25085-9_67In general, large amount of segmented and labeled data is needed to estimate statistical language understanding systems. In recent years, different approaches have been proposed to reduce the segmentation and labeling effort by means of unsupervised o semi-supervised learning techniques. We propose an active learning approach to the estimation of statistical language understanding models that involves the transcription, labeling and segmentation of a small amount of data, along with the use of raw data. We use this approach to learn the understanding component of a Spoken Dialog System. Some experiments that show the appropriateness of our approach are also presented.Work partially supported by the Spanish MICINN under contract TIN2008-06856-C05-02, and by the Vicerrectorat d’Investigació, Desenvolupament i Innovació of the Universitat Politècnica de València under contract 20100982.García Granada, F.; Hurtado Oliver, LF.; Sanchís Arnal, E.; Segarra Soriano, E. (2011). An active learning approach for statistical spoken language understanding. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 7042:565-572. https://doi.org/10.1007/978-3-642-25085-9_67S5655727042De 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)Fraser, M., Gilbert, G.: Simulating speech systems. Computer Speech and Language 5, 81–99 (1991)Gotab, P., Bechet, F., Damnati, G.: Active learning for rule-based and corpus-based spoken labguage understanding moldes. In: IEEE Workshop Automatic Speech Recognition and Understanding (ASRU 2009), pp. 444–449 (2009)Gotab, P., Damnati, G., Becher, F., Delphin-Poulat, L.: Online slu model adaptation with a partial oracle. In: Proc. of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 2862–2865 (2010)He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006)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)Riccardi, G., Hakkani-Tur, D.: Active learning: theory and applications to automatic speech recognition. IEEE Transactions on Speech and Audio Processing 13(4), 504–511 (2005)Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. International Journal of Pattern Recognition and Artificial Intelligence 16(3), 301–307 (2002)Tur, G., Hakkani-Tr, D., Schapire, R.E.: Combining active and semi-supervised learning for spoken language understanding. Speech Communication 45, 171–186 (2005

Integrating a State-of-the-Art ASR System into the Opencast Matterhorn Platform

[EN] In this paper we present the integration of a state-of-the-art ASR system into the Opencast Matterhorn platform, a free, open-source platform to support the management of educational audio and video content. The ASR system was trained on a novel large speech corpus, known as poliMedia, that was manually transcribed for the European project transLectures. This novel corpus contains more than 115 hours of transcribed speech that will be available for the research community. Initial results on the poliMedia corpus are also reported to compare the performance of different ASR systems based on the linear interpolation of language models. To this purpose, the in-domain poliMedia corpus was linearly interpolated with an external large-vocabulary dataset, the well-known Google N-Gram corpus. WER figures reported denote the notable improvement over the baseline performance as a result of incorporating the vast amount of data represented by the Google N-Gram corpus.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755. Also supported by the Spanish Government (MIPRCV ”Consolider Ingenio 2010” and iTrans2 TIN2009-14511) and the Generalitat Valenciana (Prometeo/2009/014).Valor Miró, JD.; Pérez González De Martos, AM.; Civera Saiz, J.; Juan Císcar, A. (2012). Integrating a State-of-the-Art ASR System into the Opencast Matterhorn Platform. Communications in Computer and Information Science. 328:237-246. https://doi.org/10.1007/978-3-642-35292-8_25S237246328UPVLC, XEROX, JSI-K4A, RWTH, EML, DDS: transLectures: Transcription and Translation of Video Lectures. In: Proc. of EAMT, p. 204 (2012)Zhan, P., Ries, K., Gavalda, M., Gates, D., Lavie, A., Waibel, A.: JANUS-II: towards spontaneous Spanish speech recognition 4, 2285–2288 (1996)Nogueiras, A., Fonollosa, J.A.R., Bonafonte, A., Mariño, J.B.: RAMSES: El sistema de reconocimiento del habla continua y gran vocabulario desarrollado por la UPC. In: VIII Jornadas de I+D en Telecomunicaciones, pp. 399–408 (1998)Huang, X., Alleva, F., Hon, H.W., Hwang, M.Y., Rosenfeld, R.: The SPHINX-II Speech Recognition System: An Overview. Computer, Speech and Language 7, 137–148 (1992)Speech and Language Technology Group. Sumat: An online service for subtitling by machine translation (May 2012), http://www.sumat-project.euBroman, S., Kurimo, M.: Methods for combining language models in speech recognition. In: Proc. of Interspeech, pp. 1317–1320 (2005)Liu, X., Gales, M., Hieronymous, J., Woodland, P.: Use of contexts in language model interpolation and adaptation. In: Proc. of Interspeech (2009)Liu, X., Gales, M., Hieronymous, J., Woodland, P.: Language model combination and adaptation using weighted finite state transducers (2010)Goodman, J.T.: Putting it all together: Language model combination. In: Proc. of ICASSP, pp. 1647–1650 (2000)Lööf, J., Gollan, C., Hahn, S., Heigold, G., Hoffmeister, B., Plahl, C., Rybach, D., Schlüter, R., Ney, H.: The rwth 2007 tc-star evaluation system for european english and spanish. In: Proc. of Interspeech, pp. 2145–2148 (2007)Rybach, D., Gollan, C., Heigold, G., Hoffmeister, B., Lööf, J., Schlüter, R., Ney, H.: The rwth aachen university open source speech recognition system. In: Proc. of Interspeech, pp. 2111–2114 (2009)Stolcke, A.: SRILM - An Extensible Language Modeling Toolkit. In: Proc. of ICSLP (2002)Michel, J.B., et al.: Quantitative analysis of culture using millions of digitized books. Science 331(6014), 176–182Turro, C., Cañero, A., Busquets, J.: Video learning objects creation with polimedia. In: 2010 IEEE International Symposium on Multimedia (ISM), December 13-15, pp. 371–376 (2010)Barras, C., Geoffrois, E., Wu, Z., Liberman, M.: Transcriber: development and use of a tool for assisting speech corpora production. Speech Communication Special Issue on Speech Annotation and Corpus Tools 33(1-2) (2000)Apache. Apache felix (May 2012), http://felix.apache.org/site/index.htmlOsgi alliance. osgi r4 service platform (May 2012), http://www.osgi.org/Main/HomePageSahidullah, M., Saha, G.: Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition 54(4), 543–565 (2012)Gascó, G., Rocha, M.-A., Sanchis-Trilles, G., Andrés-Ferrer, J., Casacuberta, F.: Does more data always yield better translations? In: Proc. of EACL, pp. 152–161 (2012)Sánchez-Cortina, I., Serrano, N., Sanchis, A., Juan, A.: A prototype for interactive speech transcription balancing error and supervision effort. In: Proc. of IUI, pp. 325–326 (2012

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Bayesian Speaker Adaptation Based on a New Hierarchical Probabilistic Model

In this paper, a new hierarchical Bayesian speaker adaptation method called HMAP is proposed that combines the advantages of three conventional algorithms, maximum a posteriori (MAP), maximum-likelihood linear regression (MLLR), and eigenvoice, resulting in excellent performance across a wide range of adaptation conditions. The new method efficiently utilizes intra-speaker and inter-speaker correlation information through modeling phone and speaker subspaces in a consistent hierarchical Bayesian way. The phone variations for a specific speaker are assumed to be located in a low-dimensional subspace. The phone coordinate, which is shared among different speakers, implicitly contains the intra-speaker correlation information. For a specific speaker, the phone variation, represented by speaker-dependent eigenphones, are concatenated into a supervector. The eigenphone supervector space is also a low dimensional speaker subspace, which contains inter-speaker correlation information. Using principal component analysis (PCA), a new hierarchical probabilistic model for the generation of the speech observations is obtained. Speaker adaptation based on the new hierarchical model is derived using the maximum a posteriori criterion in a top-down manner. Both batch adaptation and online adaptation schemes are proposed. With tuned parameters, the new method can handle varying amounts of adaptation data automatically and efficiently. Experimental results on a Mandarin Chinese continuous speech recognition task show good performance under all testing conditions

Contextual Language Model Adaptation for Conversational Agents

Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under a variety of speaking styles, domains, vocabulary and argots. In this paper, we present a DNN-based method to adapt the LM to each user-agent interaction based on generalized contextual information, by predicting an optimal, context-dependent set of LM interpolation weights. We show that this framework for contextual adaptation provides accuracy improvements under different possible mixture LM partitions that are relevant for both (1) Goal-oriented conversational agents where it's natural to partition the data by the requested application and for (2) Non-goal oriented conversational agents where the data can be partitioned using topic labels that come from predictions of a topic classifier. We obtain a relative WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass decoding framework, over an unadapted model. We also show up to a 15% relative improvement in recognizing named entities which is of significant value for conversational ASR systems.Comment: Interspeech 2018 (accepted

Clustering of syntactic and discursive information for the dynamic adaptation of Language Models

Presentamos una estrategia de agrupamiento de elementos de diálogo, de tipo semántico y discursivo. Empleando Latent Semantic Analysis (LSA) agru- pamos los diferentes elementos de acuerdo a un criterio de distancia basado en correlación. Tras seleccionar un conjunto de grupos que forman una partición del espacio semántico o discursivo considerado, entrenamos unos modelos de lenguaje estocásticos (LM) asociados a cada modelo. Dichos modelos se emplearán en la adaptación dinámica del modelo de lenguaje empleado por el reconocedor de habla incluido en un sistema de diálogo. Mediante el empleo de información de diálogo (las probabilidades a posteriori que el gestor de diálogo asigna a cada elemento de diálogo en cada turno), estimamos los pesos de interpolación correspondientes a cada LM. Los experimentos iniciales muestran una reducción de la tasa de error de palabra al emplear la información obtenida a partir de una frase para reestimar la misma frase

Towards Affordable Disclosure of Spoken Word Archives

This paper presents and discusses ongoing work aiming at affordable disclosure of real-world spoken word archives in general, and in particular of a collection of recorded interviews with Dutch survivors of World War II concentration camp Buchenwald. Given such collections, the least we want to be able to provide is search at different levels and a flexible way of presenting results. Strategies for automatic annotation based on speech recognition – supporting e.g., within-document search– are outlined and discussed with respect to the Buchenwald interview collection. In addition, usability aspects of the spoken word search are discussed on the basis of our experiences with the online Buchenwald web portal. It is concluded that, although user feedback is generally fairly positive, automatic annotation performance is still far from satisfactory, and requires additional research