Text to speech system

1. A text-to-speech method configured to output speech having a selected speaker voice and a selected speaker attribute,

said method comprising:

inputting text;

dividing said inputted text into a sequence of acoustic units;

selecting a speaker for the inputted text;

selecting a speaker attribute for the inputted text;

converting said sequence of acoustic units to a sequence of speech vectors using an acoustic model; and

outputting said sequence of speech vectors as audio with said selected speaker voice and a selected speaker attribute,

wherein said acoustic model comprises a first set of parameters relating to speaker voice and a second set of parameters relating to speaker attributes, wherein the first and second set of parameters do not overlap such that each can be varied independently, wherein selecting a speaker voice comprises selecting parameters from the first set of parameters which give the speaker voice and selecting the speaker attribute comprises selecting the parameters from the second set which give the selected speaker attribute, and wherein the first set of parameters and the second set of parameters are provided in clusters.

2. A method according to claim 1 , wherein there are a plurality of sets of parameters relating to different speaker attributes and the plurality of sets of parameters do not overlap.

3. A method according to claim 1 , wherein the acoustic model comprises probability distribution functions which relate the acoustic units to the sequence of speech vectors and selection of the first and second set of parameters modifies the said probability distributions.

4. A method according to claim 3 , wherein said second parameter set is related to an offset which is added to at least some of the parameters of the first set of parameters.

5. A method according to claim 3 , wherein control of the speaker voice and attributes is achieved via a weighted sum of the means of the said probability distributions and selection of the first and second sets of parameters controls the weightings used.

6. A method according to claim 5 , wherein each cluster comprises at least one sub-cluster, and a weighting is derived for each sub-cluster.

7. A method according to claim 1 , wherein the sets of parameters are continuous such that the speaker voice is variable over a continuous range and the voice attribute is variable over a continuous range.

8. A method according to claim 1 , wherein the values of the first and second sets of parameters are defined using audio, text, an external agent or any combination thereof.

9. A method according to claim 4 , wherein the method is configured to transplant a speech attribute from a first speaker to a second speaker, by adding second parameters obtained from the speech of a first speaker to that of a second speaker.

10. A method according to claim 9 , wherein the second parameters are obtained by:

receiving speech data from the first speaker speaking with the attribute to be transplanted;

identifying speech data for the first speaker which is closest to the speech data of the second speaker;

determining the difference between the speech data obtained from the first speaker speaking with the attribute to be transplanted and the speech data of the first speaker which is closest to the speech data of the second speaker; and

determining the second parameters from the said difference.

11. A method according to claim 10 , wherein the difference is determined between the means of the probability distributions which relate the acoustic units to the sequence of speech vectors.

12. A method according to claim 10 , wherein the second parameters are determined as a function of the said difference and said function is a linear function.

13. A method according to claim 11 , wherein the identifying speech data for the first speaker which is closest to the speech data of the second speaker comprises minimizing a distance function that depends on the probability distributions of the speech data of the first speaker and the speech data of the second speaker.

14. A method according to claim 13 , wherein said distance function is a euclidean distance, Bhattacharyya distance or Kullback-Leibler distance.

15. A non-transitory computer readable carrier medium comprising computer readable code configured to cause a computer to perform the method of claim 1 .

16. A method according to claim 1 , wherein the speaker attribute is related to emotion.

17. A method of training an acoustic model for a text-to-speech system, wherein said acoustic model converts a sequence of acoustic units to a sequence of speech vectors, the method comprising:

receiving speech data from a plurality of speakers and a plurality of speakers speaking with different attributes;

isolating speech data from the received speech data which relates to speakers speaking with a common attribute;

training a first acoustic sub-model using the speech data received from a plurality of speakers speaking with a common attribute, said training comprising deriving a first set of parameters, wherein said first set of parameters are varied to allow the acoustic model to accommodate speech for the plurality of speakers;

training a second acoustic sub-model from the remaining speech, said training comprising identifying a plurality of attributes from said remaining speech and deriving a set of second parameters wherein said set of second parameters are varied to allow the acoustic model to accommodate speech for the plurality of attributes; and

outputting an acoustic model by combining the first and second acoustic sub-models such that the combined acoustic model comprises a first set of parameters relating to speaker voice and a second set of parameters relating to speaker attributes, wherein the first and second set of parameters do not overlap, and wherein selecting a speaker voice comprises selecting parameters from the first set of parameters which give the speaker voice and selecting the speaker attribute comprises selecting the parameters from the second set which give the selected speaker attribute.

18. A method according to claim 17 , wherein the acoustic model comprises probability distribution functions which relate the acoustic units to the sequence of speech vectors, and training the first acoustic sub-model comprises arranging the probability distributions into clusters, with each cluster comprises at least one sub-cluster, and wherein said first parameters are speaker dependent weights to be applied such there is one weight per sub-cluster, and

training the second acoustic sub-model comprises arranging the probability distributions into clusters, with each cluster comprises at least one sub-cluster, and wherein said second parameters are attribute dependent weights to be applied such there is one weight per sub-cluster.

19. A method according to claim 18 , wherein the received speech data containing a variety of each one of the considered voice attributes.

20. A method according to claim 18 , wherein training the model comprises repeatedly re-estimating the parameters of the first acoustic sub-model while keeping part of the parameters of the second acoustic sub-model fixed and then re-estimating the parameters of the second acoustic sub-model while keeping part of the parameters of the first acoustic model fixed until a convergence criteria is met.

21. A method according to claim 17 , wherein the different attributes are related to emotion.

22. A text-to-speech system for use for simulating speech having a selected speaker voice and a selected speaker attribute a plurality of different voice characteristics,

said system comprising:

a text input for receiving inputted text;

a processor configured to:

divide said inputted text into a sequence of acoustic units;

allow selection of a speaker for the inputted text;

allow selection of a speaker attribute for the inputted text;

convert said sequence of acoustic units to a sequence of speech vectors using an acoustic model, wherein said model has a plurality of model parameters describing probability distributions which relate an acoustic unit to a speech vector; and

output said sequence of speech vectors as audio with said selected speaker voice and a selected speaker attribute,

wherein said acoustic model comprises a first set of parameters relating to speaker voice and a second set of parameters relating to speaker attributes, wherein the first and second set of parameters do not overlap such that each can be varied independently, wherein selecting a speaker voice comprises selecting parameters from the first set of parameters which give the speaker voice and selecting the speaker attribute comprises selecting the parameters from the second set which give the selected speaker attribute and wherein the first set of parameters and the second set of parameters are provided in clusters.

23. A method according to claim 22 , wherein the speaker attribute is related to emotion.