Feature frame stacking in RNN-based Tandem ASR systems - Learned vs. predefined context

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2011, Pages 1233-1236

  Wöllmer, Martin ^a   Schuller, Björn ^a   Rigoll, Gerhard ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Automatic speech recognition; Context modeling; Long short term memory; Recurrent neural networks

Indexed keywords

AUTOMATIC SPEECH RECOGNITION; AUTOMATIC SPEECH RECOGNITION SYSTEM; CONTEXT MODELING; CONTEXTUAL INFORMATION; EMPIRICAL EVIDENCE; FEATURE LEVEL; FEATURE VECTORS; MULTI-LAYER PERCEPTRONS; MULTI-STREAM; PHONEME RECOGNITION; SHORT TERM MEMORY; TANDEM SYSTEM;

BRAIN; PATTERN RECOGNITION SYSTEMS; PROFITABILITY; RECURRENT NEURAL NETWORKS;

SPEECH RECOGNITION;

EID: 84865748400     PISSN: None     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Q. Zhu, B. Chen, N. Morgan, and A. Stolcke, "Tandem connectionist feature extraction for conversational speech recognition," in Machine Learning for Multimodal Interaction. Springer, 2005, pp. 223-231.
2. F. Grezl and P. Fousek, "Optimizing bottle-neck features for LVCSR," in Proc. of ICASSP, Las Vegas, NV, 2008, pp. 4729-4732.
3. M. Wöllmer, F. Eyben, A. Graves, B. Schuller, and G. Rigoll, "Bidirectional LSTM networks for context-sensitive keyword detection in a cognitive virtual agent framework," Cognitive Computation, vol. 2, no. 3, pp. 180-190, 2010.
4. S. Thomas, S. Ganapathy, and H. Hermansky, "Phoneme recognition using spectral envelope and modulation frequency features," in Proc. of ICASSP, Taipei,Taiwan, 2009, pp. 4453-4456.
5. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
6. A. Graves and J. Schmidhuber, "Framewise phoneme classification with bidirectional LSTM and other neural network architectures," Neural Networks, vol. 18, no. 5-6, pp. 602-610, 2005.
7. S. Fernandez, A. Graves, and J. Schmidhuber, "An application of recurrent neural networks to discriminative keyword spotting," in Proc. of ICANN, Porto, Portugal, 2007, pp. 220-229.
8. M. Wöllmer, F. Eyben, B. Schuller, and G. Rigoll, "A multi-stream ASR framework for BLSTM modeling of conversational speech," in Proc. of ICASSP, Prague, Czech Republic, 2011.
9. A. Stupakov, E. Hanusa, D. Vijaywargi, D. Fox, and J. Bilmes, "The design and collection of COSINE, a multi-microphone in situ speech corpus recorded in noisy environments," Computer Speech and Language, 2011, to appear.
10. S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies," in A Field Guide to Dynamical Recurrent Neural Networks, S. C. Kremer and J. F. Kolen, Eds. IEEE Press, 2001, pp. 1-15.
11. M. Wöllmer, B. Schuller, F. Eyben, and G. Rigoll, "Combining long short-term memory and dynamic bayesian networks for incremental emotion-sensitive artificial listening," IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 5, pp. 867-881, 2010.
12. A. Graves, S. Fernandez, M. Liwicki, H. Bunke, and J. Schmidhuber, "Unconstrained online handwriting recognition with recurrent neural networks," Advances in Neural Information Processing Systems, vol. 20, pp. 1-8, 2008.
13. M. Schuster and K. K. Paliwal, "Bidirectional recurrent neural networks," IEEE Transactions on Signal Processing, vol. 45, pp. 2673-2681, 1997.
14. F. Eyben, M. Wöllmer, and B. Schuller, "openSMILE - the Munich versatile and fast open-source audio feature extractor," in Proc. of ACM Multimedia, Firenze, Italy, 2010, pp. 1459-1462.
15. M. Wöllmer, F. Eyben, B. Schuller, and G. Rigoll, "Recognition of spontaneous conversational speech using long short-term memory phoneme predictions," in Proc. of Interspeech, Makuhari, Japan, 2010, pp. 1946-1949.