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The premier, best-selling guide to Taxes for Small Businesses. With filing day around the corner, learn how to figure out your tax situation today! The premier guide to understanding Accounting for small businesses. Knowing your numbers is very important. Figure out your numbers and more today! Share your thoughts with other customers. Write a customer review. There was a problem filtering reviews right now. You cannot usually tell the size of your corpus from the number of utterances you have. Sometimes utterances are very long, and at other times they may be as short as a single word or sound.
The best way to estimate the size of your corpus in hours is to look at the total size in bytes of all utterance files which you can use to train your models. Speech data are usually stored in integer format. Sample Size: If your sampling is "8bit" then every integer has 1 byte associated with it. I found faligned transcripts for all but utterances, and those utterances have no transcripts that I can find. Should I leave them out of the training? I don't think it will make that much difference at its 1.
Also, for this much data, how many senones should I use? A: Leave out utterances for which you don't have transcripts unless you have very little data in the first place, in which case hear out the audio and transcribe it yourself.
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In this case, just leave them out. Should I force-align my transcripts before I train? A: The process of force-alignment takes an existing transcript, and finds out which, among the many pronunciations for the words occuring in the transcript, are the correct pronunciations. The data corresponding to the phone AX will now be wrongly used to train the phone AA. Then go all the way back and re-train your ci models with the new transcripts.
Q: I don't have transcripts. How can I force-align? A: you cannot force-align any transcript that you do not have. Q: I am going to first train a set of coarse models to force-align the transcripts.
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So I should submit begining and end silence marked transcripts to the trainer for the coarse models. Do you think the trainer will consider them as fillers instead of normal words? Q: I have a huge collection of data recorded under different conditions. I would like to train good speaker-independent models using this or a subset of this data. How should I select my data? I also suspect that some of the transcriptions are not very accurate, but I can't figure out which ones are inaccurate without listening to all the data.
If the broad acoustic conditions are similar for example, if all your data has been recorded off TV shows , it is best to use all data you can get for training speaker-independent bandwidth-independent models, gender-independent models. If you suspect that some of the data you are using might be bad for some reason, then during the baum-welch iterations you can monitor the likelihoods corresponding to each utterance and discard the really low-likelihood utterances. The fourth field in the control file is simply an utterance identifier.
So long as that field and the entry at the end of the corresponding utterance in the transcript file are the same, you can have anything written there and the training will go through. It is only a very convenient tag.
The particular format that you see for the fourth field is just an "informative" way of tagging. Usually we use file paths and names alongwith other file attributes that are of interest to us. Q: I am trying to train with Switchboard data.
Switchboard data is mulaw encoded. Do we have generic tools for converting from stereo mulaw to standard raw file? Here is a conversion table for converting 8 bit mulaw to 16 bit PCM.
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A: Thumb rule figures for the number of senones that you should be training are given in the following table: Amount of training data hours No. For clean speech you may choose to use any odd number of states, depending on the amount of data you have and the type of acoustic units you are training. If you are training word models, for example, you might be better off using 5 states or higher.
You cannot currently train 1 state hmms with the Sphinx. Remember that the topology is also related to the frame rate and the minimum expected duration of your basic sound units. For example the phoneme "T" rarely lasts more than ms. If your frame rate is frames per second, "T" will therefore be represented in no more than 3 frames.
If you use a 5 state noskip topology, this would force the recognizer to use at least 5 frames to model the phone. Even a 7 state topology that permits skips between alternate states would force the recognizer to visit at least 4 of these states, thereby requring the phone to be at least 4 frames long. Both would be erroneous.
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Give this point very serious thought before you decide on your HMM topology. If you are not convinced, send us a mail and we'll help you out. Q: I have two sets of models, A and B. The set A has been trained with 10, tied states or senones and B has been trained with 5, senones. If I want to compare the recognition results on a third database using A and B, does this difference in the number of senones matter? If A and B have been optimally trained i.
I just want want to train a set of coarse models for forced-alignment. The baum-welch iterations are very slow. In 24 hours, it has only gone through utterances.
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I have total 16, utterances. As this speed, it will take 20 days for the first iteration of baum-welch, considering the convergence ratio to be 0. Is there any way to speed this up? A: If you start from flat-initialized models the first two iterations of baum welch will always be very slow. This is because all paths through the utterance are similar and the algorithm has to consider all of them.
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In the higher iterations, when the various state distributions begin to differ from each other, the computation speeds up a great deal. Given the observed speed of your machine, you cannot possibly hope to train your models on a single machine. You may think of assigning a lower value to the "topn" argument of the bw executable, but since you are training CI models, changing the topn value from its default 99 to any smaller number will not affect the speed, since there is only at best 1 Gaussian per state anyway throughout the computation.
Try to get more machines to share the jobs. There is a -npart option to help you partition your training data. Alternatively, you can shorten your training set, since you only want to use the models for forced alignment. Models trained with about 10 hours of data will do the job just as well. However, when I try to decode noisy speech with my models, the decoder just dies.
Adding noise to the test data increases the mismatch between the models and test data. So if the models are not really well trained and hence not very generalizable to slightly different data , the decoder dies. There are multiple resons for this: The decoder cannot find any valid complete paths during decoding. All paths that lead to a valid termination may get pruned out The likelihood of the data may be so poor that the decoder goes into underflow.
The likelihood of this one model becomes very small and the resulting low likelihood get inverted to a very large positive number becuase the decoder uses integer arithmetic, and results in segmentation errors, artihmetic errors, etc. One way to solve this problem is just to retrain with noisy data.
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