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How to improve the traditional ASR using Connectionist Temporal Classification

The traditional Automatic Speech Recognition (ASR) performs at about 85% accuracy rate.  At this rate, ASR users are often frustrated with the experience with using such a system.

The tradition ASR is often fragile:

1) requires extensive modification of parameters, just to make it work.
2) requires extensive understanding of a language model and a acoustic model.
3) doesn't scale well to multiple languages.
4) hyper-sensitive to speaker variants.


Deep Learning on the acoustic model has been introduced, but not much of gain in the accuracy.

What if, we can do a DL from end to end?








Connectionist Temporal Classification (2006) introduces an idea of using FFT on the frequency of a recording of a voice command and constructs a spectrogram at 8kHz.  At each spectrogram interval, a DL neural network can be assigned, individually.



The basic idea is to have RNN output neurons to encode distribution over "symbols".

The traditional ASR uses a phoneme-based model or a graphme-based model.  Again, suspectible to speaker variants.  e.g. If a speaker speaks slowly 'hello' over 10 seconds or 5 seconds, how do we map each phoneme to a neuron?

CTC allows the temporaral mapping to each DNN/RNN neuron by using softmax on top of a dense layer to provide the best possibility model. 





Using DNN/RNN, train the model over many many days on a high-end compute machine, we were able to have the model to transcribe a voice recording in English.  Our accuracy is around 92%, and it is not sensitive to speaker variants.

Trainging Deep Speech Recognition (NLP) is tricky.  The idea is to use SortaGrad (Bengio et al, ICML 2009)





References

• Gales and Young. “The Applica,on of Hidden Markov Models in Speech Recogni,on” Founda,ons and Trends in Signal Processing, 2008. 
• Jurafsky and Mar,n. “Speech and Language Processing”. Pren,ce Hall, 2000. 
• Bourlard and Morgan. “CONNECTIONIST SPEECH RECOGNITION: A Hybrid Approach”. Kluwer Publishing, 1994. 
• A Graves, S Fernández, F Gomez, J Schmidhuber. “Connec,onist temporal classifica,on: labelling unsegmented sequence data with recurrent neural networks.” ICML, 2006. 
• Hannun, Maas, Jurafsky, Ng. “First-­‐Pass Large Vocabulary Con,nuous Speech Recogni,on using Bi-­‐Direc,onal Recurrent DNNs” ArXiv: 1408.2873 
• Hannun, et al. “Deep Speech: Scaling up end-­‐to-­‐end speech recogni,on”. ArXiv:1412.5567 
• H. Hermansky, "Perceptual linear predic,ve (PLP) analysis of speech", J. Acoust. Soc. Am., vol. 87, no. 4, pp. 1738-­‐1752, Apr. 1990. 
• H. Hermansky and N. Morgan, "RASTA processing of speech", IEEE Trans. on Speech and Audio Proc., vol. 2, no. 4, pp. 578-­‐589, Oct. 1994. 
• H. Schwenk, “Con,nuous space language models”, 2007. 







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