public int Recognize(ISoundSignalReader signal, HiddenMarkovModel[] models, out string name,
SignalVisitor voiceVisitor = null)
{
var featureUtility = new FeatureUtility(_engineParameters);
signal.Reset();
var features = featureUtility.ExtractFeatures(signal, voiceVisitor).First();
var observations = _codeBook.Quantize(features.Select(item => new Point(item)).ToArray());
var likelyHoodValue = Double.MinValue;
name = string.Empty;
var index = 0;
var ret = 0;
foreach (var model in models)
{
var val = model.Evaluate(observations);
if (val > likelyHoodValue)
{
likelyHoodValue = val;
name = model.Tag.ToString();
ret = index;
}
index++;
}
return ret;
}
public int Recognize(ISoundSignalReader signal, HiddenMarkovClassifier hmm, out string name,
SignalVisitor voiceVisitor = null)
{
var featureUtility = new FeatureUtility(_engineParameters);
signal.Reset();
var features = featureUtility.ExtractFeatures(signal, voiceVisitor).First();
var observations = _codeBook.Quantize(features.Select(item => new Point(item)).ToArray());
double[] responsabilities;
var ret = hmm.Compute(observations, out responsabilities);
var models = hmm.Models;
var likelyHoodValue = Double.MinValue;
name = string.Empty;
foreach (var model in models)
{
var val = model.Evaluate(observations);
if (val > likelyHoodValue)
{
likelyHoodValue = val;
name = model.Tag.ToString();
}
}
return ret;
}
public VoiceActivitySignalReader(ISoundSignalReader signal, int frameSize, int emptyFrames = 3,
VoiceActivationDetectionEnhancement enhancements = VoiceActivationDetectionEnhancement.All)
{
_voiceActivityDetection = new VoiceActivityDetection(signal, frameSize, emptyFrames, enhancements);
signal.Reset();
_soundSignal = signal;
_properties = new Dictionary<string, object> {{VoiceProperty, false}};
SupportedPropertiesSet.Add(VoiceProperty);
}
public int Recognize(ISoundSignalReader signal, out string name,
SignalVisitor visitor = null)
{
var featureUtility = new FeatureUtility(_engineParameters);
signal.Reset();
var features = featureUtility.ExtractFeatures(signal, visitor).First();
var observations = _codeBook.Quantize(features.Select(item => new Point(item)).ToArray());
var likelyHoodValue = Double.MinValue;
HiddenMarkovModel bestFit = null;
var index = 0;
var ret = 0;
foreach (var model in _models.Values)
{
var val = model.Evaluate(observations);
if (val > likelyHoodValue)
{
likelyHoodValue = val;
bestFit = model;
ret = index;
}
index++;
}
if (bestFit != null)
{
var idProp = (IdentificationProperties) bestFit.Tag;
name = idProp.Label;
}
else
name = string.Empty;
return ret;
}
