/** Tests whether working with different types transformations works properly. */
@Test
public void testUnivariateMeanTransformation() {
float mean = 20;
float var = 0.001f;
MixtureComponent gaussian =
new MixtureComponent(
new float[] {mean},
new float[][] {{2}},
new float[] {5},
new float[] {var},
null,
null);
Assert.assertTrue(
LogMath.getLogMath().logToLinear(gaussian.getScore(new float[] {2 * mean + 5})) > 10);
}
public void newProperties(PropertySheet ps) throws PropertyException {
logMath = LogMath.getLogMath();
logger = ps.getLogger();
unitManager = (UnitManager) ps.getComponent(PROP_UNIT_MANAGER);
hmmManager = new HMMManager();
contextIndependentUnits = new LinkedHashMap<String, Unit>();
phoneList = new LinkedHashMap<String, Integer>();
meanTransformationMatrixPool = createDummyMatrixPool("meanTransformationMatrix");
meanTransformationVectorPool = createDummyVectorPool("meanTransformationMatrix");
varianceTransformationMatrixPool = createDummyMatrixPool("varianceTransformationMatrix");
varianceTransformationVectorPool = createDummyVectorPool("varianceTransformationMatrix");
String modelName = ps.getString(MODEL_NAME);
String location = ps.getString(LOCATION);
String phone = ps.getString(PHONE_LIST);
String dataDir = ps.getString(DATA_DIR);
logger.info("Creating Sphinx3 acoustic model: " + modelName);
logger.info(" Path : " + location);
logger.info(" phonelist : " + phone);
logger.info(" dataDir : " + dataDir);
// load the HMM model file
boolean useCDUnits = ps.getBoolean(PROP_USE_CD_UNITS);
assert !useCDUnits;
try {
loadPhoneList(
ps,
useCDUnits,
StreamFactory.getInputStream(location, phone),
location + File.separator + phone);
} catch (StreamCorruptedException sce) {
printPhoneListHelp();
} catch (IOException e) {
e.printStackTrace();
}
}
/**
* Compute the density values of a sampled interval with an univariate <code>MixtureComponent
* </code> and compare values with the precomputed-computed ones.
*/
@Test
public void testUnivariateDensity() {
float minX = 10;
float maxX = 30;
float resolution = 0.1f;
float mean = 20;
float var = 3;
MixtureComponent gaussian = new MixtureComponent(new float[] {mean}, new float[] {var});
for (float curX = minX; curX <= maxX; curX += resolution) {
double gauLogScore = gaussian.getScore(new FloatData(new float[] {curX}, 16000, 0));
double manualScore =
(1 / sqrt(var * 2 * PI)) * exp((-0.5 / var) * (curX - mean) * (curX - mean));
double gauScore = LogMath.getLogMath().logToLinear((float) gauLogScore);
Assert.assertEquals(manualScore, gauScore, 1E-5);
}
}