153,272 research outputs found
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.; GarcÃa Granada, F.; SanchÃs 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., GarcÃa, 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)BenedÃ, 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
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.GarcÃa Granada, F.; Hurtado Oliver, LF.; SanchÃs 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., GarcÃa, 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
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
Robustness issues in a data-driven spoken language understanding system
Robustness is a key requirement in spoken language understanding (SLU) systems. Human speech is often ungrammatical and ill-formed, and there will frequently be a mismatch between training and test data. This paper discusses robustness and adaptation issues in a statistically-based SLU system which is entirely data-driven. To test robustness, the system has been tested on data from the Air Travel Information Service (ATIS) domain which has been artificially corrupted with varying levels of additive noise. Although the speech recognition performance degraded steadily, the system did not fail catastrophically. Indeed, the rate at which the end-to-end performance of the complete system degraded was significantly slower than that of the actual recognition component. In a second set of experiments, the ability to rapidly adapt the core understanding component of the system to a different application within the same broad domain has been tested. Using only a small amount of training data, experiments have shown that a semantic parser based on the Hidden Vector State (HVS) model originally trained on the ATIS corpus can be straightforwardly adapted to the somewhat different DARPA Communicator task using standard adaptation algorithms. The paper concludes by suggesting that the results presented provide initial support to the claim that an SLU system which is statistically-based and trained entirely from data is intrinsically robust and can be readily adapted to new applications
Deep learning systems as complex networks
Thanks to the availability of large scale digital datasets and massive
amounts of computational power, deep learning algorithms can learn
representations of data by exploiting multiple levels of abstraction. These
machine learning methods have greatly improved the state-of-the-art in many
challenging cognitive tasks, such as visual object recognition, speech
processing, natural language understanding and automatic translation. In
particular, one class of deep learning models, known as deep belief networks,
can discover intricate statistical structure in large data sets in a completely
unsupervised fashion, by learning a generative model of the data using
Hebbian-like learning mechanisms. Although these self-organizing systems can be
conveniently formalized within the framework of statistical mechanics, their
internal functioning remains opaque, because their emergent dynamics cannot be
solved analytically. In this article we propose to study deep belief networks
using techniques commonly employed in the study of complex networks, in order
to gain some insights into the structural and functional properties of the
computational graph resulting from the learning process.Comment: 20 pages, 9 figure
Scaling Recurrent Neural Network Language Models
This paper investigates the scaling properties of Recurrent Neural Network
Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and
address the questions of how RNNLMs scale with respect to model size,
training-set size, computational costs and memory. Our analysis shows that
despite being more costly to train, RNNLMs obtain much lower perplexities on
standard benchmarks than n-gram models. We train the largest known RNNs and
present relative word error rates gains of 18% on an ASR task. We also present
the new lowest perplexities on the recently released billion word language
modelling benchmark, 1 BLEU point gain on machine translation and a 17%
relative hit rate gain in word prediction
- …