/// <summary>
/// Gets the pre-selection triphone for each leaf nodes in pre-selection forest.
/// </summary>
/// <param name="forest">The pre-selection forest.</param>
/// <param name="labels">The given list of the labels in training set.</param>
/// <returns>A Dictionary whose key is a Label to indicate a triphone, whose value is leaf nodes in forest.</returns>
public static Dictionary<Label, DecisionTreeNode> GetPreselectionTriphone(this DecisionForest forest, List<Label> labels)
{
Dictionary<Label, DecisionTreeNode> triphoneToNode = new Dictionary<Label, DecisionTreeNode>();
// In order to get the all pre-selection tri-phone, all full-context label in training set will be visited.
foreach (Label label in labels)
{
// Get the tri-phone label from the full-context label.
Label triphone = new Label(label);
triphone.ResizeFeatureValue(LabelFeatureNameSet.Triphone);
// If the tri-phone exists, it can be ignored. However, we can process it again to perform verifications.
if (!triphoneToNode.ContainsKey(triphone))
{
// Travel the forest using the full-context label to get the matched leaf nodes.
List<DecisionTreeNode> nodes = FilterDecisionForest(forest, label);
// Please notice: in pre-selection tree, each label only match one leaf node since the pre-selection tree is clustered by phone model.
if (nodes.Count != 1)
{
throw new InvalidDataException("The forest is not a invalid pre-selection forest.");
}
triphoneToNode.Add(triphone, nodes[0]);
}
}
return triphoneToNode;
}
/// <summary>
/// Extracts the features of the given utterance.
/// </summary>
/// <param name="sentId">
/// Sentence id.
/// </param>
/// <param name="utterance">
/// Service Provider utterance object.
/// </param>
/// <returns>
/// The sentence contains all the features.
/// </returns>
/// <exception cref="InvalidDataException">
/// Exception.
/// </exception>
public Sentence Extract(string sentId, TtsUtterance utterance)
{
List<FeatureVector> vectors;
try
{
// Then, extracts the features.
vectors = ExtractionEngine.Extract(utterance, FeatureMetas);
}
catch (EspException e)
{
throw new InvalidDataException(Helper.NeutralFormat("Extract feature error on sentence \"{0}\"",
sentId), e);
}
// Validates the extracted vectors.
if (vectors.Count != FeatureMetas.Count)
{
throw new InvalidDataException(
Helper.NeutralFormat("Length of result is mismatch on sentence \"{0}\"", sentId));
}
for (int i = 0; i < vectors.Count; i++)
{
if (vectors[i].Count != utterance.Phones.Count)
{
throw new InvalidDataException(
Helper.NeutralFormat("Length of vector is mismatch on sentence \"{0}\"", sentId));
}
}
// Creates a sentence to store all the features.
Sentence sentence = new Sentence { Id = sentId };
for (int i = 0; i < vectors[0].Count; ++i)
{
// Create candidates for each phoneme.
PhoneSegment p = new PhoneSegment
{
Sentence = sentence,
Index = i,
Features = vectors.Select(v => v[i])
.Skip(LabelFeatureNameSet.MandatoryFeatureNames.Length).ToArray(),
};
// Create the label to store the features.
Label label = new Label(FeatureNameSet);
for (int j = 0; j < vectors.Count; ++j)
{
if (vectors[j][i].ValueType == FeatureValueType.FEATURE_VALUE_TYPE_UNKOWN)
{
label.SetFeatureValue(FeatureNameSet.FeatureNames[j], Label.NotApplicableFeatureValue);
}
else if (FeatureMetas[j].Property == TtsFeatureProperty.TTS_FEATURE_PROPERTY_PHONE_ID)
{
Phone phone = PhoneSet.GetPhone(vectors[j][i].IntValue);
label.SetFeatureValue(FeatureNameSet.FeatureNames[j], Offline.Phoneme.ToHtk(phone.Name));
}
else
{
label.SetFeatureValue(FeatureNameSet.FeatureNames[j],
vectors[j][i].IntValue.ToString(CultureInfo.InvariantCulture));
}
// Updates the corresponding value records.
FeatureValueRecords[j].Update(vectors[j][i]);
}
p.Label = label;
sentence.PhoneSegments.Add(p);
}
return sentence;
}
/// <summary>
/// Build cross correlation matrix.
/// </summary>
/// <param name="phoneList">Mono phone list.</param>
/// <returns>Cross correlation matrix.</returns>
public List<Pair<CandidateGroup, CandidateGroup>> BuildCCMatrix(IEnumerable<string> phoneList)
{
List<Pair<CandidateGroup, CandidateGroup>> matrix = new List<Pair<CandidateGroup, CandidateGroup>>();
foreach (CandidateGroup group in CandidateGroups)
{
Label label = new Label(LabelFeatureNameSet.Triphone) { Text = group.Name };
foreach (string phone in phoneList)
{
string triphone = Helper.NeutralFormat("{0}-{1}+{2}", label.CentralPhoneme, label.RightPhoneme, phone);
if (_nameIndexedCandidateGroup.ContainsKey(triphone))
{
Pair<CandidateGroup, CandidateGroup> pair =
new Pair<CandidateGroup, CandidateGroup>(group, _nameIndexedCandidateGroup[triphone]);
matrix.Add(pair);
}
}
}
return matrix;
}
/// <summary>
/// Finds the corresponding candidate in the given sentence set.
/// </summary>
/// <param name="sentenceSet">The given sentence set.</param>
/// <param name="sentId">The sentence id which contains the candidate.</param>
/// <param name="indexOfNonSilence">The index of non-silence unit of the candidate.</param>
/// <param name="label">The label of the candidate.</param>
/// <returns>The corresponding candidate.</returns>
private static UnitCandidate FindCandidate(TrainingSentenceSet sentenceSet, string sentId, int indexOfNonSilence, string label)
{
if (!sentenceSet.Sentences.ContainsKey(sentId))
{
throw new InvalidDataException(Helper.NeutralFormat("Cannot find the sentence \"{0}\"", sentId));
}
Sentence sentence = sentenceSet.Sentences[sentId];
UnitCandidate result = null;
foreach (UnitCandidate candidate in sentence.Candidates)
{
if (candidate.IndexOfNonSilence == indexOfNonSilence)
{
result = candidate;
break;
}
}
if (result == null)
{
throw new InvalidDataException(Helper.NeutralFormat("Cannot find the candidate \"{0}:{1}\"", sentId, indexOfNonSilence));
}
Label myLabel = new Label { Text = label };
if (result.Label.CentralPhoneme != myLabel.CentralPhoneme)
{
throw new InvalidDataException(
Helper.NeutralFormat(
"Mismatched full-context label, expected current phone \"{0}\" but \"{1}\"",
result.Label.CentralPhoneme,
myLabel.CentralPhoneme));
}
return result;
}
/// <summary>
/// Gets the all leaf nodes which can match the given label in the decision forst.
/// </summary>
/// <param name="forest">The given decision forest.</param>
/// <param name="label">The given label.</param>
/// <returns>A List object contains all the matched leaf nodes.</returns>
public static List<DecisionTreeNode> FilterDecisionForest(this DecisionForest forest, Label label)
{
List<DecisionTreeNode> nodes = new List<DecisionTreeNode>();
// For each decision tree in forest, it need to be traveled.
foreach (DecisionTree tree in forest.TreeList)
{
// Firstly, the tree should be matched by phone name if it is a phone-dependent tree.
bool matched;
string phone = tree.Phone;
if (Phoneme.IsAnyPhone(phone))
{
matched = true;
}
else
{
matched = label.CentralPhoneme == phone;
}
if (matched)
{
// Travel the matched tree, each tree will return a single leaf node.
nodes.Add(FilterTree(tree.NodeList[0], forest.Questions, label));
}
}
return nodes;
}
/// <summary>
/// Gets the all possible tri-phone from this set.
/// </summary>
/// <returns>The list of Label object to hold the tri-phone.</returns>
public List<Label> GetAllTriphone()
{
List<Label> list = new List<Label>();
foreach (string left in LeftPhones)
{
foreach (string central in CentralPhones)
{
foreach (string right in RightPhones)
{
Label label = new Label(LabelFeatureNameSet.Triphone);
label.LeftPhoneme = left;
label.CentralPhoneme = central;
label.RightPhoneme = right;
list.Add(label);
}
}
}
return list;
}
/// <summary>
/// Filters a tree to get the leaf node.
/// </summary>
/// <param name="node">The first node of the tree.</param>
/// <param name="questions">A Dictionary to contains all the Question used by the tree.</param>
/// <param name="label">The given label.</param>
/// <returns>The leaf node which match the label.</returns>
public static DecisionTreeNode FilterTree(DecisionTreeNode node, IDictionary<string, Question> questions, Label label)
{
while (node.NodeType != DecisionTreeNodeType.Leaf)
{
Question question = questions[node.QuestionName];
// If the value set of question contains the feature value, it means matched with question mark.
if (MatchHtsQuestion(question.ValueSet, label.GetFeatureValue(question.FeatureName)))
{
node = node.RightChild;
}
else
{
node = node.LeftChild;
}
}
return node;
}
/// <summary>
/// Initializes a new instance of the Label class as a copy of the given one.
/// </summary>
/// <param name="label">The given label to copy.</param>
public Label(Label label)
{
_featureNames = label._featureNames;
Text = label.Text;
}
