1. SPEECH RECOGNITION Presented to Dr. V. Kepuska Presented by Lisa & Za ECE 5526

2. How does Sphinx3 work?  Sphinx3 uses ---HMM with continuous probability density function  Flat initialization state: - Mixture weights: the weights given to every Gaussian in the Gaussian mixture corresponding to a state - transition matrices: the matrix of state transition probabilities - means: means of all Gaussians - variances: variances of all Gaussians

3. How does Sphinx3 work?  forward-backward re-estimation algorithm (Baum-Welch algorithm) - Use for converging the likelihood training  Untied Modeling - Training for all context-dependent phones (usually triphones) that are seen in the training corpus

4. How does Sphinx3 work?  Building decision tree - Used to decide which of the HMM states of all the triphones (seen and unseen) are similar to each other  Pruning the decision trees

5. Our project:::Spelling Bees  Use Sphinx3 to train the recorded data  Compare the train data with the test data Result: We have used 224 train data and 73 test data. The dictionary has 46 words and 33 phones are used.  32.7% word error rate and 49.3% sentence error rate

6. The result:::

7.  id: (fash-cen2-fash-b)  Scores: (#C #S #D #I) 3 0 0 0  REF: a m y  HYP: a m y  Speaker sentences 1: moe #utts: 8  id: (moe-m_oses1)  Scores: (#C #S #D #I) 4 0 1 1  REF: * m o s e S  HYP: E m o s e *  Eval: I D   id: (moe-m_oses2)  Scores: (#C #S #D #I) 5 0 0 0  REF: m o s e s  HYP: m o s e s  Eval:

8. Reference:  http://www.speech.cs.cmu.edu/sphinxman/fr4.html http://www.speech.cs.cmu.edu/sphinxman/fr4.html  Lecture notes from Speech recognition class  http://www.ele.uri.edu/~hansenj/projects/ele5 85/ http://www.ele.uri.edu/~hansenj/projects/ele5 85/  makeraw.m  record.m