Sphinx3 Forced Alignment with different arguments, edit-distance grammar generation

(Author: Srikanth Ronanki)

(Status: GSoC 2012 Pronunciation Evaluation Week 2)

Following last week's discussion describing how to obtain phoneme acoustic scores from sphinx3_align, here is some additional detail pertaining to two of the necessary output arguments:

1. Following up on the discussion at https://sourceforge.net/projects/cmusphinx/forums/forum/5471/topic/4583225, I was able to produce acoustic scores for each frame, and thereby also for each phoneme in a single recognition pass. Add the following code to thewrite_stseg function in main_align.c and use the state segmentation parameter -stsegdir as an argument to the program:

char str2[1024];

align_stseg_t *tmp1;

for (i = 0, tmp1 = stseg; tmp1; i++, tmp1 = tmp1->next) {

mdef_phone_str(kbc->mdef, tmp1->pid, str2);

fprintf(fp, "FrameIndex %d Phone %s PhoneID %d SenoneID %d state %d Ascr %11d \n", i, str2, tmp1->pid, tmp1->sen, tmp1->state, tmp1->score);


2. By using the phone segmentation parameter -phsegdir as an argument to the program, the acoustic scores for each phoneme can be calculated. The output sequence for the word "approach" is as follows:

SFrm  EFrm   SegAScr       Phone

0     9    -64725           SIL

10    21    -63864       AH SIL P b

22    33   -126819       P AH R i

34    39    -21470       R P OW i

40    51    -69577       OW R CH i

52    64    -55937       CH OW DH e

Each phoneme in the "Phone" column is represented as .  The full command line usage for this output is:

$ sphinx3_align -hmm wsj_all_cd30.mllt_cd_cont_4000 -dict cmu.dic -fdict phone.filler -ctl phone.ctl -insent phone.insent -cepdir feats -phsegdir phonesegdir -phlabdir phonelabdir -stsegdir statesegdir -wdsegdir aligndir -outsent phone.outsent

Work in progress:

1. It's very important to weight word scores by the words' part of speech (articles don't matter very much if they are omitted, but nouns, adjectives, verbs, and adverbs are the most important.)

2. I put some exemplar recordings for three phrases the project mentor had collected at http://talknicer.net/~ronanki/Datasets/ in each subdirectory there for each of the three phrases.  The description of the phrases is at http://talknicer.net/~ronanki/Datasets/files/phrases.txt.

3. I ran sphinx3_align for that sample data set. I wrote a program to calculate mean and standard deviations of phoneme acoustic scores, and the mean duration of each phoneme. I also generated neighbor phonemes for each of the phrases, and the output is written in this file: http://talknicer.net/~ronanki/Datasets/out_ngb_phonemes.insent

4. I also tried some of the other sphinx3 executables such as sphinx3_decode, sphinx3_livepretend, andsphinx3_continous for mispronunciation detection. For the sentence, "Approach the teaching of pronunciation with more confidence." (phrase 1), I used this command:

$ SPHINX3DECODE -hmm ${WSJ} -fsg phone.fsg -dict basicphone.dic -fdict phone.filler -ctl new_phone.ctl -hyp phone.out -cepdir feats -mode allphone -hypseg phone_hypseg.out -op_mode 2

The decoder, sphinx3_decode, produced this output:


The forced alignment system, sphinx3_align, produced this output:


The sphinx3_livepretend and sphinx3_continous commands produce output in words using language models and acoustic models along with a complete dictionary of expected words:

Approach to teaching opponents the nation with more confidence

Plans for the coming week:

1. Write and test audio upload and pronunciation evaluation for per-phoneme standard scores.

2. Since there are many deletions in the edit distance scoring grammars tried so far, we need to modify the grammar file and/or the method we are using to detect whether neighboring phonemes match more closely. Here is my idea of finding neighboring phonemes by dynamic programming:

a. Run the decoder to get the best possible output

b. Align the decoder output to forced-alignment output using a dynamic programming string matching algorithm

c. The aligned output will have the same number of phones as from forced alignment. So, we need to test two things for each phoneme:

  • If the phone is same as expected phoneme, no need to do anything
  • If the phone is not as expected phoneme, check that phone in the list of neighboring phonemes of the expected phoneme.

d. Then, we can run sphinx3_align with this outcome against the same wav file to check whether the acoustic scores actually indicate a better match.

3. As an alternative to the above, I used sox to split each input wave file in to individual phoneme wav files using the forced alignment phone labels, and then used a separate recognition pass on each tiny speech segment. Now, I am writing separate grammar files for the neighboring phonemes for each phoneme. Once I complete them, I will check the output using decoder for each phoneme segment. This should provide for more accurate assessment of mispronunciations.

4. I will update the wiki here at https://cmusphinx.github.io/wiki/pronunciation_evaluation with my current tasks and milestones.

Using OpenGrm NGram Library for the encoding of joint multigram language models as WFST.

(author: John Salatas)

This article will review the OpenGrm NGram Library [1] and its usage for language modeling in ASR. OpenGrm makes use of functionality in the openFST library [2] to create, access and manipulate n-gram language models and it can be used as the language model training toolkit for integrating phonetisaurus' model training procedures [3] into a simplified application.

1. Model Training
Having the aligned corpus produced from cmudict by the aligner code of our previous article [4], the first step is to generate an OpenFst-style symbol table for the text tokens in input corpus. This can be done with: [5]

# ngramsymbols < cmudict.corpus > cmudict.syms

Given the symbol table, a text corpus can be converted to a binary finite state archive (far) [6] with:

# farcompilestrings --symbols=cmudict.syms --keep_symbols=1 cmudict.corpus > cmudict.far

Next step is to count n-grams from an input corpus, converted in FAR format. It produces an n-gram model in the FST format. By using the switch --order the maximum length n-gram to count can be chosen.

The 1-gram through 9-gram counts for the cmudict.far finite-state archive file created above can be created with:

# ngramcount --order=9 cmudict.far > cmudict.cnts

Finally the 9-gram counts in cmudict.cnts above can be converted to a WFST model with:

# ngrammake --method="kneser_ney" cmudict.cnts > cmudict.mod

The --method option is used for selecting the smoothing method [7] from one of the six available:

  • witten_bell: smooths using Witten-Bell [8], with a hyperparameter k, as presented in [9].
  • absolute: smooths based on Absolute Discounting [10], using bins and discount parameters.
  • katz: smooths based on Katz Backoff [11], using bins parameters.
  • kneser_ney: smooths based on Kneser-Ney [12], a variant of Absolute Discounting.
  • presmoothed: normalizes at each state based on the n-gram count of the history.
  • unsmoothed: normalizes the model but provides no smoothing.

2. Evaluation – Comparison with phonetisaurus

In order to evaluate OpenGrm models, ther procedure described above was repeated using the standard 90%-10% split of the cmudict into a training and test set respectively. The binary fst format produced by ngrammake wasn't readable by the phonetisaurus evaluation script, so it was converted to ARPA format with:

# ngramprint --ARPA cmudict.mod > cmudict.arpa

and then back to a phonetisaurus binary fst format with:

# phonetisaurus-arpa2fst --input=cmudict.arpa --prefix="cmudict/cmudict"

Finally the test set was evaluated with

# evaluate.py --modelfile cmudict/cmudict.fst --testfile cmudict.dict.test --prefix cmudict/cmudict
Words: 13328 Hyps: 13328 Refs: 13328
(T)otal tokens in reference: 84955
(M)atches: 77165 (S)ubstitutions: 7044 (I)nsertions: 654 (D)eletions: 746
% Correct (M/T) -- %90.83
% Token ER ((S+I+D)/T) -- %9.94
% Accuracy 1.0-ER -- %90.06
(S)equences: 13328 (C)orrect sequences: 8010 (E)rror sequences: 5318
% Sequence ER (E/S) -- %39.90
% Sequence Acc (1.0-E/S) -- %60.10

3. Conclusion – Future Works

The evaluation results above are, as expected, almost identical with those produced by the phonetisaurus' procedures and the use of MITLM toolkit instead of OpenGrm.

Having the above description, the next step is to integrate all of the commands above into a simplified application, combined with the dictionary alignment code introduced in our previous article [13].


[1] OpenGrm NGram Library

[2] openFST library

[3] Phonetisaurus: A WFST-driven Phoneticizer – Framework Review

[4] Porting phonetisaurus many-to-many alignment python script to C++

[5] OpenGrm NGram Library Quick Tour

[6] OpenFst Extensions: FST Archives (FARs)

[7] S.F. Chen, G. Goodman, “An Empirical Study of Smoothing Techniques for Language Modeling”, Harvard Computer Science Technical report TR-10-98, 1998.

[8] I. H. Witten, T. C. Bell, "The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression", IEEE Transactions on Information Theory 37 (4), pp. 1085–1094, 1991.

[9] B. Carpenter, "Scaling high-order character language models to gigabytes", Proceedings of the ACL Workshop on Software, pp. 86–99, 2005.

[10] H. Ney, U. Essen, R. Kneser, "On structuring probabilistic dependences in stochastic language modeling", Computer Speech and Language 8, pp. 1–38, 1994.

[11] S. M. Katz, "Estimation of probabilities from sparse data for the language model component of a speech recogniser", IEEE Transactions on Acoustics, Speech, and Signal Processing 35 (3), pp. 400–401, 1987.

[12] R. Kneser, H. Ney, "Improved backing-off for m-gram language modeling", Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). pp. 181–184, 1995.

[13] Porting phonetisaurus many-to-many alignment python script to C++

Building a Java application with Apache Nutch and Solr

Apache Nutch is a scalable web crawler that supports Hadoop. Apache Solr is a complete search engine that is built on top of Apache Lucene. In this tutorial, we make a simple Java application that crawls "World" section of CNN.com with Apache Nutch and uses Solr to index them. We are going to use both of them as libraries, which means Solr must be working without a servlet and HTTP connections. We will be using Eclipse as the IDE. This is going to be a long tutorial, so get yourself a cup of your favorite drink.

Troy: GSoC 2012 Pronunciation Evaluation Week 2

Although quite frustrated with what i have encountered in the last few days, everyday i have to struggle with my own research and stay up to work on the open source project, but all my efforts are not recognized at all. Anyway it's better to keep a log of what i have done and then take some time to think how things should go on.

These are the things I've accomplished in the second week of GSoC 2012:

1. Set up a cron job for the rtmplite server to automatically check whether the process is still running or not. If it is stopped, restart it. This will allow the server to stay up if the machine gets rebooted, and will allow the server to spawn subprocesses without being stopped by job control as happens when the process is put into the background from a terminal shell. To accomplish this, I first created a .process file in my home directory with the rtmplite server's process id number as its sole contents. You can use 'top' or 'ps' to find out the process id of the server. Then I created this shell script file to check the status of the rtmplite server process:

if [ -e "$pidfile" ]
    # check whether the process is running
    rtmppid=`/usr/bin/head -n 1 ${pidfile} | /usr/bin/awk '{print $1}'`;
    # restart the process if not running
    if [ ! -d /proc/${rtmppid} ]
       /usr/bin/python ${exefile} -r ${dataroot} &
       echo "${rtmppid}" > ${pidfile}
       echo `/bin/date` "### rtmplite process restarted with pid: ${rtmppid}"

This script first checks whether the .process file exists or not. If we don't want the cron job to check for this process temporarily (such as when we apply patches to the program), we can simply delete this file and it won't check on or try to restart the server; after out maintenance, recreate the file with the new process id, and the checking will automatically resume.

The last and also the most important step is to schedule this task in cron by creating following item with the command
crontab -e

* * * * * [path_to_the_script]/check_status.sh

This causes the cron system to run this script every minute, thereby checking the rtmplite server process every minute.
2. Implemented web server user login and registration pages using MySQL and HTML. We use a MySQL database for storing user information, so I designed and created this table for user information in the server's mysql database:

Field Type Comments
userid INTEGER Compulsory, automatically increased, primary key
email VARCHAR(200) Compulsory, users are identified by emails
password VARCHAR(50) Compulsory, encrypted using SHA1, at least 8 alphanumeric characters
name VARCHAR(100) Not compulsory, default 'NULL'
age INTEGER Not compulsory, default 'NULL', accepted values [0,150]
sex CHAR(1) Not compulsory, default 'NULL', accepted values {'M', 'F'}
native CHAR(1) Not compulsory, default 'NULL', accepted values {'Y', 'N'}. Indicating the user is a native English speaker or not.
place VARCHAR(1000) Not compulsory, default 'NULL'. Indicating the place when the user lived at the age between 6 and 8.
accent CHAR(1) Not compulsory, default 'NULL', accepted values {'Y', 'N'}. Indicating the user has a self-reported accent or not.

This table was created by the following SQL command:

   email VARCHAR(200) NOT NULL,
   password VARCHAR(50) NOT NULL,
   name VARCHAR(100),
   age INTEGER,
   sex SET('M', 'F'),
   native SET('Y', 'N') DEFAULT 'N',
   place VARCHAR(1000),
   accent SET('Y', 'N'),
   CONSTRAINT chk_age CHECK (age>=0 AND age<=150)

I also prototyped the login and simple registration pages are in HTML. Here are their preliminary screenshots:

If you like, you can go to this page to help us test the system: http://talknicer.net/~li-bo/datacollection/login.php. On the server, we use PHP to retrive the form information from the login and registration pages, perform an update or query in mysql database, and then send data back in HTML.

The recording interface, has also been modified to use HTML instead of pure Flex as earlier. The page currently displays well, but there is no event interaction between HTML and Flash

3. Database schema design for the entire project: Several SQL tables have been designed to store the various information used by all aspects of this project. Detailed table information can be found on our wiki page: http://talknicer.net/w/Database_schema. Here is a brief discussion.

First, the
user table shown above will be augmented to keep two additional kinds of user information: one for normal student users and one for those who are providing exemplar recordings. Student users, when they can provide correct pronunciation, should also be allowed to contribute to the exemplar recordings. Also if exemplar recorders register through the website, they have to show they are proficient enough to contribute a qualified exemplar recording, so we should be able to use the student evaluation system to qualify them for uploading exemplar contributions.

There are several other tables for additional information such as
languages for a list of languages defined by the ISO in case we may extend our project to other languages; a region table to store some idea of the user's accent; prompts table for the list of text resources will be used for pronunciation evaluation. Then are also tables to log the recordings the users do and tables for set of tests stored in the system.

Here are my plans for the coming week:

1. Discuss details of the game specification to finish the last part of schema design.

2. Figure out how to integrate the Flash audio recorder with the HTML interface using bidirectional communication between ActionScript and JavaScript.

3. Implement the student recording interface.

4. Further tasks can be found at: http://talknicer.net/w/To_do_list