Stochastic Modelling: From Pattern Classification to Speech Recognition and Language Translation

This paper gives an overview of the stochastic modelling approach to machine translation. Starting with the Bayes decision rule as in pattern classification and speech recognition, we show how the resulting system architecture can be structured into three parts: the language model probability, the string translation model probability and the search procedure that gener-ates the word sequence in the target language. We discuss the properties of the system components and report results on the translation of spoken dialogues in the VERBMOBIL project. The experience obtained in the VERB-MOBIL project, in particular a large-scale end-to-end evaluation, showed that the stochastic modelling approach resulted in significantly lower error rates than three competing translation approaches: the sentence error rate was 29 % in comparison with 52 % to 62% for the other translation approaches.

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

Multimodal Grounding for Sequence-to-Sequence Speech Recognition

Humans are capable of processing speech by making use of multiple sensory modalities. For example, the environment where a conversation takes place generally provides semantic and/or acoustic context that helps us to resolve ambiguities or to recall named entities. Motivated by this, there have been many works studying the integration of visual information into the speech recognition pipeline. Specifically, in our previous work, we propose a multistep visual adaptive training approach which improves the accuracy of an audio-based Automatic Speech Recognition (ASR) system. This approach, however, is not end-to-end as it requires fine-tuning the whole model with an adaptation layer. In this paper, we propose novel end-to-end multimodal ASR systems and compare them to the adaptive approach by using a range of visual representations obtained from state-of-the-art convolutional neural networks. We show that adaptive training is effective for S2S models leading to an absolute improvement of 1.4% in word error rate. As for the end-to-end systems, although they perform better than baseline, the improvements are slightly less than adaptive training, 0.8 absolute WER reduction in single-best models. Using ensemble decoding, end-to-end models reach a WER of 15% which is the lowest score among all systems.Comment: ICASSP 201

Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition

In this paper, we investigate the use of invariant features for speaker recognition. Owing to their characteristics, these features are introduced to cope with the difficult and challenging problem of sensor variability and the source of performance degradation inherent in speaker recognition systems. Our experiments show: (1) the effectiveness of these features in match cases; (2) the benefit of combining these features with the mel frequency cepstral coefficients to exploit their discrimination power under uncontrolled conditions (mismatch cases). Consequently, the proposed invariant features result in a performance improvement as demonstrated by a reduction in the equal error rate and the minimum decision cost function compared to the GMM-UBM speaker recognition systems based on MFCC features