# Conversational Speech Transcription Using Context-Dependent Deep Neural Networks

@inproceedings{Seide2011ConversationalST, title={Conversational Speech Transcription Using Context-Dependent Deep Neural Networks}, author={Frank Seide and Gang Li and Dong Yu}, booktitle={ICML}, year={2011} }

Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, combine the classic artificial-neural-network HMMs with traditional context-dependent acoustic modeling and deep-belief-network pre-training. CD-DNN-HMMs greatly outperform conventional CD-GMM (Gaussian mixture model) HMMs: The word error rate is reduced by up to one third on the difficult benchmarking task of speaker-independent single-pass transcription of telephone conversations.

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#### References

SHOWING 1-10 OF 20 REFERENCES

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

- Computer Science
- IEEE Transactions on Audio, Speech, and Language Processing
- 2012

A pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output that can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs. Expand

Deep Belief Networks for phone recognition

- Computer Science
- 2009

Deep Belief Networks (DBNs) have recently proved to be very effective in a variety of machine learning problems and this paper applies DBNs to acous ti modeling. Expand

Context-dependent connectionist probability estimation in a hybrid hidden Markov model-neural net speech recognition system

- Computer Science
- Comput. Speech Lang.
- 1994

A new training procedure that "smooths" networks with different degrees of context dependence is proposed to obtain a robust estimate of the context-dependent probabilities of the HMM/MLP speaker-independent continuous speech recognition system. Expand

ACID/HNN: clustering hierarchies of neural networks for context-dependent connectionist acoustic modeling

- Computer Science
- Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)
- 1998

It is argued that a hierarchical approach is crucial in applying locally discriminative connectionist models to the typically very large state spaces observed in LVCSR systems. Expand

Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition

- Computer Science
- 2010

It is shown that pre-training can initialize weights to a point in the space where fine-tuning can be effective and thus is crucial in training deep structured models and in the recognition performance of a CD-DBN-HMM based large-vocabulary speech recognizer. Expand

Recent innovations in speech-to-text transcription at SRI-ICSI-UW

- Computer Science
- IEEE Transactions on Audio, Speech, and Language Processing
- 2006

It is shown that acoustic adaptation can be improved by predicting the optimal regression class complexity for a given speaker, and speech modeling innovations include the use of a syntax-motivated almost-parsing language model, as well as principled vocabulary-selection techniques. Expand

Connectionist probability estimators in HMM speech recognition

- Computer Science
- IEEE Trans. Speech Audio Process.
- 1994

It is shown that a connectionist component improves a state-of-the-art HMM system through a statistical interpretation of connectionist networks as probability estimators. Expand

Learning representations by back-propagating errors

- Computer Science
- Nature
- 1986

Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain. Expand

A Fast Learning Algorithm for Deep Belief Nets

- Mathematics, Computer Science
- Neural Computation
- 2006

A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. Expand