Abstract: A text-to-speech method for simulating a plurality of different voice characteristics includes dividing inputted text into a sequence of acoustic units; selecting voice characteristics for the inputted text; converting the sequence of acoustic units to a sequence of speech vectors using an acoustic model having a plurality of model parameters provided in clusters each having at least one sub-cluster and describing probability distributions which relate an acoustic unit to a speech vector; and outputting the sequence of speech vectors as audio with the selected voice characteristics. A parameter of a predetermined type of each probability distribution is expressed as a weighted sum of parameters of the same type using voice characteristic dependent weighting. In converting the sequence of acoustic units to a sequence of speech vectors, the voice characteristic dependent weights for the selected voice characteristics are retrieved for each cluster such that there is one weight per sub-cluster.

Abstract: A text-to-speech method configured to output speech having a selected speaker voice and a selected speaker attribute, including: inputting text; dividing the inputted text into a sequence of acoustic units; selecting a speaker for the inputted text; selecting a speaker attribute for the inputted text; converting the sequence of acoustic units to a sequence of speech vectors using an acoustic model; and outputting the sequence of speech vectors as audio with the selected speaker voice and a selected speaker attribute. The acoustic model includes a first set of parameters relating to speaker voice and a second set of parameters relating to speaker attributes, which parameters do not overlap. The selecting a speaker voice includes selecting parameters from the first set of parameters and the selecting the speaker attribute includes selecting the parameters from the second set of parameters.

Abstract: A text to speech method, the method comprising: receiving input text; dividing said inputted text into a sequence of acoustic units; converting 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 outputting said sequence of speech vectors as audio, the method further comprising determining at least some of said model parameters by: extracting expressive features from said input text to form an expressive linguistic feature vector constructed in a first space; and mapping said expressive linguistic feature vector to an expressive synthesis feature vector which is constructed in a second space.

Abstract: A method for identifying a plurality of speakers in audio data and for decoding the speech spoken by said speakers; the method comprising: receiving speech; dividing the speech into segments as it is received; processing the received speech segment by segment in the order received to identify the speaker and to decode the speech, processing comprising: performing primary decoding of the segment using an acoustic model and a language model; obtaining segment parameters indicating the differences between the speaker of the segment and a base speaker during the primary decoding; comparing the segment parameters with a plurality of stored speaker profiles to determine the identity of the speaker, and selecting a speaker profile for said speaker; updating the selected speaker profile; performing a further decoding of the segment using a speaker independent acoustic model, adapted using the updated speaker profile; outputting the decoded speech for the identified speaker, wherein the speaker profiles are upd

Abstract: A text-to-speech method configured to output speech having a selected speaker voice and a selected speaker attribute, including: inputting text; dividing the inputted text into a sequence of acoustic units; selecting a speaker for the inputted text; selecting a speaker attribute for the inputted text; converting the sequence of acoustic units to a sequence of speech vectors using an acoustic model; and outputting the sequence of speech vectors as audio with the selected speaker voice and a selected speaker attribute. The acoustic model includes a first set of parameters relating to speaker voice and a second set of parameters relating to speaker attributes, which parameters do not overlap. The selecting a speaker voice includes selecting parameters from the first set of parameters and the selecting the speaker attribute includes selecting the parameters from the second set of parameters.

Abstract: A text-to-speech method for simulating a plurality of different voice characteristics includes dividing inputted text into a sequence of acoustic units; selecting voice characteristics for the inputted text; converting the sequence of acoustic units to a sequence of speech vectors using an acoustic model having a plurality of model parameters provided in clusters each having at least one sub-cluster and describing probability distributions which relate an acoustic unit to a speech vector; and outputting the sequence of speech vectors as audio with the selected voice characteristics. A parameter of a predetermined type of each probability distribution is expressed as a weighted sum of parameters of the same type using voice characteristic dependent weighting. In converting the sequence of acoustic units to a sequence of speech vectors, the voice characteristic dependent weights for the selected voice characteristics are retrieved for each cluster such that there is one weight per sub-cluster.

Abstract: A method for identifying a plurality of speakers in audio data and for decoding the speech spoken by said speakers; the method comprising: receiving speech; dividing the speech into segments as it is received; processing the received speech segment by segment in the order received to identify the speaker and to decode the speech, processing comprising: performing primary decoding of the segment using an acoustic model and a language model; obtaining segment parameters indicating the differences between the speaker of the segment and a base speaker during the primary decoding; comparing the segment parameters with a plurality of stored speaker profiles to determine the identity of the speaker, and selecting a speaker profile for said speaker; updating the selected speaker profile; performing a further decoding of the segment using a speaker independent acoustic model, adapted using the updated speaker profile; outputting the decoded speech for the identified speaker, wherein the speaker profiles are upd

Abstract: The present invention relates to a word-spotting system and a method for finding a keyword in ascoustic data. The method includes a filler recognition phase and a keyword recognition phase wherein: during the filler recognition phase the acoustic data is processed to identify phones and to generate temporal delimiters and likelihood scores for the phones; during the keyword recognition phase, the acoustic data is processed to identify instances of a specified keyword including a sequence of phones; wherein the temporal delimiters and likelihood scores generated in the filler recognition phase are used in the keyword recognition phase.