Advances in All-Neural Speech Recognition

This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology

The Microsoft 2017 Conversational Speech Recognition System

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set

Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure

Active learning for dialogue act labelling

Active learning is a useful technique that allows for a considerably reduction of the amount of data we need to manually label in order to reach a good performance of a statistical model. In order to apply active learning to a particular task we need to previously define an effective selection criteria, that picks out the most informative samples at each iteration of active learning process. This is still an open problem that we are going to face in this work, in the task of dialogue annotation at dialogue act level. We present two different criteria, weighted number of hypothesis and entropy, that we have applied to the Sample Selection Algorithm for the task of dialogue act labelling, that retrieved appreciably improvements in our experimental approach. © 2011 Springer-Verlag.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV “Consolider Ingenio 2010” program (CSD2007-00018), MITTRAL (TIN2009-14633-C03-01) projects and the FPI scholarship (BES-2009-028965). Also supported by the Generalitat Valenciana under grant Prometeo/2009/014 and GV/2010/067Ghigi, F.; Tamarit Ballester, V.; Martínez-Hinarejos, C.; Benedí Ruiz, JM. (2011). Active learning for dialogue act labelling. En Lecture Notes in Computer Science. Springer Verlag (Germany). 6669:652-659. https://doi.org/10.1007/978-3-642-21257-4_81S6526596669Alcácer, N., Benedí, J.M., Blat, F., Granell, R., Martínez, C.D., Torres, F.: Acquisition and Labelling of a Spontaneous Speech Dialogue Corpus. In: SPECOM, Greece, pp. 583–586 (2005)Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in spanish: DIHANA. In: Fifth LREC, Genova, Italy, pp. 1636–1639 (2006)Bunt, H.: Context and dialogue control. THINK Quarterly 3 (1994)Casacuberta, F., Vidal, E., Picó, D.: Inference of finite-state transducers from regular languages. Pat. Recognition 38(9), 1431–1443 (2005)Dybkjær, L., Minker, W. (eds.): Recent Trends in Discourse and Dialogue. Text, Speech and Language Technology, vol. 39. Springer, Dordrecht (2008)Gorin, A., Riccardi, G., Wright, J.: How may I help you? Speech Comm. 23, 113–127 (1997)Hwa, R.: Sample selection for statistical grammar induction. In: Proceedings of the 2000 Joint SIGDAT, pp. 45–52. Association for Computational Linguistics, Morristown (2000)Lavie, A., Levin, L., Zhan, P., Taboada, M., Gates, D., Lapata, M.M., Clark, C., Broadhead, M., Waibel, A.: Expanding the domain of a multi-lingual speech-to-speech translation system. In: Proceedings of the Workshop on Spoken Language Translation, ACL/EACL 1997 (1997)Martínez-Hinarejos, C.D., Tamarit, V., Benedí, J.M.: Improving unsegmented dialogue turns annotation with N-gram transducers. In: Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation (PACLIC23), vol. 1, pp. 345–354 (2009)Robinson, D.W.: Entropy and uncertainty, vol. 10, pp. 493–506 (2008)Stolcke, A., Coccaro, N., Bates, R., Taylor, P., van Ess-Dykema, C., Ries, K., Shriberg, E., Jurafsky, D., Martin, R., Meteer, M.: Dialogue act modelling for automatic tagging and recognition of conversational speech. Computational Linguistics 26(3), 1–34 (2000)Tamarit, V., Benedí, J., Martínez-Hinarejos, C.: Estimating the number of segments for improving dialogue act labelling. In: Proceedings of the First International Workshop of Spoken Dialog Systems Technology (2009)Young, S.: Probabilistic methods in spoken dialogue systems. Philosophical Trans. Royal Society (Series A) 358(1769), 1389–1402 (2000

Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song

A pervasive belief with regard to the differences between human language and animal vocal sequences (song) is that they belong to different classes of computational complexity, with animal song belonging to regular languages, whereas human language is superregular. This argument, however, lacks empirical evidence since superregular analyses of animal song are understudied. The goal of this paper is to perform a superregular analysis of animal song, using data from gibbons as a case study, and demonstrate that a superregular analysis can be effectively used with non-human data. A key finding is that a superregular analysis does not increase explanatory power but rather provides for compact analysis: Fewer grammatical rules are necessary once superregularity is allowed. This pattern is analogous to a previous computational analysis of human language, and accordingly, the null hypothesis, that human language and animal song are governed by the same type of grammatical systems, cannot be rejected.Comment: Accepted for publication by Royal Society Open Scienc