Acoustic Modeling using Deep Belief Network For Bangla Automatic Speech Recognition

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Published on February 25, 2014

Author: kaidul

Source: slideshare.net

Acoustic Modeling using DBN For Bangla ASR Presented by Mahtab Ahmed Roll- 0907006 Kaidul islam Roll-0907016 Supervised by Md. Faijul Amin

Introduction • Speech recognition and understanding of spontaneous speech have been a goal of research since 1970. • Several researches have been undergone for English ASR, perhaps not that much for Bangla yet.

Acoustic Model • An acoustic model contains statistical representations of each of the distinct sounds that makes up a word.

Acoustic Model • Pronunciation dictionary is “HOUSE” can be– – – – HOUSAND HOUSDEN HOUSE HOUSE'S [HOUSAND] [HOUSDEN] [HOUSE] [HOUSE'S] hh aw s ax n d hh aw s d ax n hh aw s hh aw s ix z • Each Phoneme is associated with a HMM.

Why DBN? • GMMs has been used along with EM Algorithm in the context of speech recognition. • It can model at any accuracy except it can not model data that lie on the non linear state space. • DBN that have many hidden layers and trained by backpropagation can outperform GMM.

RBM • Stochastic neural nets. • Only one layer of hidden units • Bipartite graph

RBM • It is An energy based model. • Can be learned by gradient descent on the log likelihood of the training data.

Training RBM • If the data are binary data then the updating of units is as follows. • Speech data are Gaussian data. Then updating becomes

Stack Of RBM

Stack Of RBM • First a GRBM is trained to model a window of frames. • Then the binary units of GRBM is used as data for training the next RBM and the procedure is repeated as many time it required. • So after learning a DBN by training a stack of RBMs Reverse weight is used to form it as Feed forward DNN

Contrastive Divergence Algorithm • Very surprising short-cut • Start with a training vector on the visible units • Updates all hidden units in parallel. • Update visible units to get a reconstruction

Speech as Multinomial data

Training DBN

Generative Pretraining

Generative Pretraining • The idea is to learn one layer of feature detectors at a time with the states of the feature detectors in one layer acting as the data for the next layer. • Multiple layers of feature detectors can be learned by undirected model. • It uses a joint distribution

Simulation by program Training Data & Reconstruction

Simulation by program Training Data (blue) & Reconstruction (red) Superimposed

Problem with Speech Recognition • difficult to identify phonemes perfectly in noisy speech. • ambiguous. WRECK A NICE BEACH Can be pronounced as RECOGNIZE SPEECH • Which words are likely to come next have to be known.

Overall Timeline and progress so far

Thank you.

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