Grapheme to phoneme alignment method and relative rule-set generating system
Grapheme-to-phoneme alignment quality is improved by introducing a first preliminary alignment step, followed by an enlargement step of the grapheme-set and phoneme-set, and a second alignment step based on the previously enlarged grapheme /phoneme sets. During the enlargement step, grapheme clusters and phoneme clusters are generated that become members of a new grapheme and phoneme set. The new elements are chosen using statistical information calculated using the results of the first alignment step. The enlarged sets are the new grapheme and phoneme alphabet used for the second alignment step. The lexicon is rewritten using this new alphabet before starting with the second alignment step that produces the final result.
1. A method of generating grapheme-to-phoneme rules for text-to-speech conversion based on a lexicon having words and phonetic transcriptions associated with the words, executed by a computer programmed to perform the method, the method comprising:
an alignment phase, using the computer, for aligning phonemes, belonging to a phoneme set, to graphemes, belonging to a grapheme set; and
a rule-set extraction phase, using the computer, for generating a set of rules for automatic grapheme to phoneme conversion, said alignment phase comprising the following steps:
aligning said lexicon in a preliminary alignment step, using the computer, by generating a first plurality of grapheme and phoneme clusters, each cluster comprising a sequence of at least two components;
enlarging at least one of said phoneme and grapheme sets, using the computer, by adding at least one of the grapheme or phoneme clusters generated in said preliminary alignment step into at least one of the phoneme and grapheme sets;
rewriting said lexicon, using the computer, according to said at least one enlarged phoneme and grapheme sets;
aligning said lexicon in a further alignment step, using the computer, by generating a second plurality of phoneme and grapheme clusters; and
the steps of:
a) selecting, using the computer, potential grapheme clusters whose occurrence is higher than a first predetermined threshold;
b) enlarging, using the computer, said grapheme set by adding said selected potential grapheme clusters;
c) selecting, using the computer, potential phoneme clusters whose occurrence is higher than a second predetermined threshold;
d) enlarging, using the computer, said phoneme set by adding said selected potential phoneme clusters; and
e) rewriting, using the computer, said lexicon by replacing each sequence of components of corresponding grapheme and phoneme clusters in said lexicon with the selected potential grapheme and phoneme clusters,
f) generating, using the computer, a lexicon alignment for said rule-set extraction phase in the further alignment step, and
g) calculating, using the computer, a statistical distribution of the second plurality of grapheme and phoneme clusters generated in said further alignment step, and repeating, using the computer, said steps a) to f) in case a number of said grapheme and phoneme clusters generated in said further alignment step is greater than a third predetermined threshold.
2. The method according to claim 1 , wherein said first predetermined threshold is equal to said second predetermined threshold.
3. The method according to claim 1 , wherein said preliminary alignment step comprises:
a1) aligning, using the computer, a lexicon in a lexicon alignment step by generating the first plurality of grapheme and phoneme clusters, each cluster comprising a sequence of at least two components;
a2) calculating, using the computer, a statistical distribution of potential grapheme and phoneme clusters generated in said lexicon alignment step;
a3) selecting, using the computer, among said potential grapheme and phoneme clusters a cluster having highest occurrence; and
a4) if said highest occurrence is higher than a third predetermined threshold, rewriting, using the computer, said lexicon by replacing each sequence of components of corresponding clusters in said lexicon with said selected cluster and repeating steps a1 to a4.
4. The method according to claim 3 , wherein said potential grapheme and phoneme clusters are individuated searching all grapheme or phoneme cancellations or insertions.
5. The method according to claim 1 , wherein said further alignment step comprises:
g1) aligning, using the computer, a lexicon in a lexicon alignment step by generating the second plurality of grapheme and phoneme clusters, each cluster comprising a sequence of at least two components;
g2) calculating, using the computer, a statistical distribution of potential grapheme and phoneme clusters generated in said lexicon alignment step;
g3) selecting, using the computer, among said potential grapheme and phoneme clusters a cluster having highest occurrence; and
g4) if said highest occurrence is higher than a third predetermined threshold, rewriting, using the computer, said lexicon by replacing each sequence of components of corresponding clusters in said lexicon with said selected cluster and repeating steps g1 to g4.
6. The method according to claim 5 , wherein said lexicon alignment step comprises:
h) generating, using the computer, a first statistical grapheme to phoneme association model having uniform probability;
i) selecting, using the computer, lexicon tuples having a total number of graphemes or grapheme clusters equal to a total number of phonemes or phoneme clusters;
j) aligning, using the computer, said tuples using said first statistical grapheme to phoneme association model;
k) recalculating, using the computer, said first statistical grapheme to phoneme association model using said aligned tuples;
l) if said recalculated model is not stable, repeating the step of aligning said tuples using said recalculated model and repeating the step of recalculating said model;
m) aligning, using the computer, the whole lexicon using said recalculated statistical grapheme to phoneme association model;
n) recalculating, using the computer, said statistical grapheme to phoneme association model using said whole lexicon; and
o) if said recalculated model is not stable, repeating the step of aligning the whole lexicon using said recalculated model and repeating the step of recalculating said model using said whole lexicon.
7. The method according to claim 1 , wherein said step of enlarging said grapheme set comprises:
c1) enlarging, using the computer, said grapheme set by adding said selected potential grapheme clusters if a number of said selected potential grapheme clusters is higher than a third predetermined threshold;
c2) lowering, using the computer, said third predetermined threshold; and, repeating steps a) and b) if the number of said selected potential grapheme clusters is lower than a predetermined number of grapheme clusters.
8. The method according to claim 1 , wherein said step of enlarging said phoneme set comprises:
e1) enlarging, using the computer, said phoneme set by adding said selected potential phoneme clusters if a number of said selected potential phoneme clusters is higher than a third predetermined threshold; and
e2) lowering, using the computer, said third predetermined threshold; repeating steps c) and d) if the number of said selected potential phoneme clusters is lower than a predetermined number of phoneme clusters.
9. The method according to claim 3 , wherein said lexicon alignment step comprises:
h) generating, using the computer, a first statistical grapheme to phoneme association model having uniform probability;
i) selecting, using the computer, lexicon tuples having a total number of graphemes or grapheme clusters equal to a total number of phonemes or phoneme clusters;
j) aligning, using the computer, said tuples using said first statistical grapheme to phoneme association model;
k) recalculating, using the computer, said first statistical grapheme to phoneme association model using said aligned tuples;
l) if said recalculated model is not stable, repeating the step of aligning said tuples using said recalculated model and repeating the step of recalculating said model;
m) aligning, using the computer, the whole lexicon using said recalculated statistical grapheme to phoneme association model;
n) recalculating, using the computer, said statistical grapheme to phoneme association model using said whole lexicon; and
o) if said recalculated model is not stable, repeating the step of aligning the whole lexicon using said recalculated model and repeating the step of recalculating said model using said whole lexicon.
m) aligning, using the computer, the whole lexicon using said recalculated statistical grapheme to phoneme association model;
n) recalculating, using the computer, said statistical grapheme to phoneme association model using said whole lexicon; and
o) if said recalculated model is not stable, repeating the step of aligning the whole lexicon using said recalculated model and repeating the step of recalculating said model using said whole lexicon.
10. A non-transitory computer readable medium encoded with a computer program product, loadable into a memory of at least one computer, the computer program product comprising computer program code portions for performing all the steps of any one of claims 1 , 2 , and 3 to 6 when said program is run on the at least one computer.
11. A rule-set generating system for generating grapheme-to-Phoneme rules from a lexicon having words and their associated phonetic transcriptions, comprising a computer readable medium, the computer readable medium comprising:
an alignment unit, stored on the computer readable medium, for the assignment of phonemes to graphemes; and
a rule-set extraction unit, stored on the computer readable medium, for generating a set of rules for automatic grapheme to phoneme conversion,
wherein said alignment unit operates according to the method of claim 1 .
12. A text to speech system for converting input text into an output acoustic signal, according to a set of rules for automatic grapheme to phoneme conversion generated by a rule-set generating system, said rule-set generating system comprising a computer readable medium, the computer readable medium comprising:
an alignment unit, stored on the computer readable medium, for the assignment of phonemes to graphemes; and
a rule-set extraction unit, stored on the computer readable medium, for generating said set of rules,
wherein said alignment unit operates according to the method of claim 1 .