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US5668926A
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Method and apparatus for converting text into audible signals using a neural network
Assignee: Motorola Solutions Inc
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Filing: 1996-03-22
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Granted: 1997-09-16
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Expires: 2016-03-22
Abstract
Abstract Text may be converted to audible signals, such as speech, by first training a neural network 106 using recorded audio messages 204. To begin the training, the recorded audio messages are converted into a series of audio frames 205 having a fixed duration 213. Then, each audio frame is assigned a phonetic representation 203 and a target acoustic representation 208, where the phonetic representation 203 is a binary word that represents the phone and articulation characteristics of the audio frame, while the target acoustic representation 208 is a vector of audio information such as pitch and energy. After training, the neural network 106 is used in conversion of text into speech. First, text that is to be convened is translated to a series of phonetic frames 401 of the same form as the phonetic representations 208 and having the fixed duration 213. Then the neural network produces acoustic representations in response to context descriptions 207 that include some of the phonetic frames 401. The acoustic representations are then converted into a speech wave form by a synthesizer 107.
Inventors
- 👤 Orhan Karaali
- 👤 Gerald Edward Corrigan
- 👤 Ira Alan Gerson
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-05 13:08
CPC Classifications
G; G10; G10L; G10L13/00; G10L13/08; G10L25/00; G10L25/27; G10L25/30
Claims Analysis
Total Claims: 37
Claim 1
Independent Claim
416 words
A method for training and utilizing a neural network that is used to convert text streams into audible signals, the method comprising the steps of: wherein training a neural network utilizes the steps of: 1a) inputting recorded audio messages; 1b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; 1c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; 1d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; 1e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; 1f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: 1g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; 1h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; 1i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and 1j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 2
Independent Claim
416 words
A method for training and utilizing a neural network that is used to convert text streams into audible signals, the method comprising the steps of: wherein training a neural network utilizes the steps of: 1a) inputting recorded audio messages; 1b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; 1c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; 1d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; 1e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; 1f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: 1g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; 1h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; 1i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and 1j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 3
Dependent Claim
15 words
The method of claim 1, wherein, in step (c) the phonetic representation includes a phone.
Claim 4
Dependent Claim
45 words
The method of claim 2, wherein, in step (c) the phonetic representation includes a binary word, where one bit of the binary word is set and any remaining bits of the binary word are not set to indicate that the phonetic representation is a phone.
Claim 5
Dependent Claim
17 words
The method of claim 1, wherein, in step (e) the plurality of acoustic representations are speech parameters.
Claim 6
Dependent Claim
18 words
The method of claim 1, wherein step (f) comprises training the neural network using back propagation of errors.
Claim 7
Dependent Claim
19 words
The method of claim 1, wherein, in step (g) the text stream is a phonetic form of a language.
Claim 8
Independent Claim
557 words
A method for training and utilizing a neural network that is used to convert text streams into audible signals, the method comprising the steps of: a) inputting recorded audio messages; b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations; d) generating a context description of a plurality of context descriptions for the each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; f) training a neural network to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation, wherein training the neural network includes the steps of: 1a) inputting recorded audio messages; 2b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; 1c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics: 1d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frames and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; 1e) assigning for the each audio frame, a target acoustic representation of a plurality of acoustic representations; 2f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the taract acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: 1g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; 1h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; 1i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and 1j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 9
Independent Claim
557 words
A method for training and utilizing a neural network that is used to convert text streams into audible signals, the method comprising the steps of: a) inputting recorded audio messages; b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations; d) generating a context description of a plurality of context descriptions for the each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; f) training a neural network to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation, wherein training the neural network includes the steps of: 1a) inputting recorded audio messages; 2b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; 1c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics: 1d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frames and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; 1e) assigning for the each audio frame, a target acoustic representation of a plurality of acoustic representations; 2f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the taract acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: 1g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; 1h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; 1i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and 1j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 10
Dependent Claim
15 words
The method of claim 7, wherein, in step (c) the phonetic representation includes a phone.
Claim 11
Dependent Claim
45 words
The method of claim 8, wherein, in step (c) the phonetic representation includes a binary word, where one bit of the binary word is set and any remaining bits of the binary word are not set to indicate that the phonetic representation is a phone.
Claim 12
Dependent Claim
15 words
The method of claim 7, wherein, in step (e) the phonetic representation includes articulation characteristics.
Claim 13
Dependent Claim
17 words
The method of claim 7, wherein, in step (f) the plurality of acoustic representations are speech parameters.
Claim 14
Dependent Claim
17 words
The method of claim 7, wherein, in step (f) the neural network is a feed-forward neural network.
Claim 15
Dependent Claim
18 words
The method of claim 7, wherein step (f) comprises training the neural network using back propagation of errors.
Claim 16
Dependent Claim
17 words
The method of claim 7, wherein, in step (f) the neural network has a recurrent input structure.
Claim 17
Dependent Claim
40 words
The method of claim 7, wherein step (d) further comprises generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames.
Claim 18
Dependent Claim
40 words
The method of claim 7, wherein step (d) further comprises generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames.
Claim 19
Dependent Claim
44 words
The method of claim 7, wherein step (d) further comprises generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames.
Claim 20
Independent Claim
523 words
A method for training and utilizing a neural network that is used to convert text streams into audible signals, the method comprising the steps of: a) receiving a text stream; b) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of a plurality of phonetic representations, and wherein the phonetic frame has a fixed duration; c) assigning one of a plurality of context descriptions to the phonetic frame based on one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; d) converting, by a neural network, the phonetic frame into one of a plurality of acoustic representations, based on the one of the plurality context descriptions, wherein training the neural network includes the steps of: d1) inputting recorded audio messages; d2) dividing the recorded audio messages into a series of audio frames wherein each audio frame has a fixed duration; d3) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; d4) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frames and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; d5) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; d6) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: d7) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; d8) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; d9) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and e) converting the one of the plurality of acoustic representations into an audible signal.
Claim 21
Independent Claim
523 words
A method for training and utilizing a neural network that is used to convert text streams into audible signals, the method comprising the steps of: a) receiving a text stream; b) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of a plurality of phonetic representations, and wherein the phonetic frame has a fixed duration; c) assigning one of a plurality of context descriptions to the phonetic frame based on one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; d) converting, by a neural network, the phonetic frame into one of a plurality of acoustic representations, based on the one of the plurality context descriptions, wherein training the neural network includes the steps of: d1) inputting recorded audio messages; d2) dividing the recorded audio messages into a series of audio frames wherein each audio frame has a fixed duration; d3) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; d4) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frames and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; d5) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; d6) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: d7) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; d8) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; d9) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and e) converting the one of the plurality of acoustic representations into an audible signal.
Claim 22
Dependent Claim
15 words
The method of claim 18, wherein, in step (b) the phonetic representation includes a phone.
Claim 23
Dependent Claim
45 words
The method of claim 19, wherein, in step (b) the phonetic representation includes a binary word, where one bit of the binary word is set and any remaining bits of the binary word are not set to indicate that the phonetic representation is a phone.
Claim 24
Dependent Claim
15 words
The method of claim 18, wherein, in step (b) the phonetic representation includes articulation characteristics.
Claim 25
Dependent Claim
17 words
The method of claim 18, wherein, in step (d) the plurality of acoustic representations are speech parameters.
Claim 26
Dependent Claim
17 words
The method of claim 18, wherein, in step (d) the neural network is a feed-forward neural network.
Claim 27
Dependent Claim
17 words
The method of claim 18, wherein, in step (d) the neural network has a recurrent input structure.
Claim 28
Dependent Claim
40 words
The method of claim 18, wherein step (c) further comprises generating syntactic boundary information based on the phonetic representation of an audio frame and a phonetic representation of at least some other audio frames of the series of audio frames.
Claim 29
Dependent Claim
40 words
The method of claim 18, wherein step (c) further comprises generating phonetic boundary information based on the phonetic representation of an audio frame and a phonetic representation of at least some other audio frames of the series of audio frames.
Claim 30
Dependent Claim
44 words
The method of claim 18, wherein step (c) further comprises generating a description of prominence of syntactic information based on the phonetic representation of an audio frame and a phonetic representation of a least some other audio frames of the series of audio frames.
Claim 31
Dependent Claim
19 words
The method of claim 18, wherein, in step (a) the text stream is a phonetic form of a language.
Claim 32
Independent Claim
536 words
A device for converting text into audible signals comprising: a text-to-phone processor, wherein the text-to-phone processor translates a text stream into a series of phonetic representations; a duration processor, operably coupled to the text-to-phone processor, wherein the duration processor generates duration data for the text stream; a pre-processor, wherein the pre-processor converts the series of phonetic representations and the duration data into a series of phonetic frames, wherein each phonetic frame of the series of phonetic frames is of a fixed duration and has a context description, and wherein the context description is based on each phonetic frame of the series of phonetic frames and at least some other phonetic frame of the series of phonetic frames; and a neural network, which can be trained, which generates an acoustic representation for each phonetic frame of the series of phonetic frames based on the context description, wherein training the neural network includes the steps of: a) inputting recorded audio messages; b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 33
Independent Claim
536 words
A device for converting text into audible signals comprising: a text-to-phone processor, wherein the text-to-phone processor translates a text stream into a series of phonetic representations; a duration processor, operably coupled to the text-to-phone processor, wherein the duration processor generates duration data for the text stream; a pre-processor, wherein the pre-processor converts the series of phonetic representations and the duration data into a series of phonetic frames, wherein each phonetic frame of the series of phonetic frames is of a fixed duration and has a context description, and wherein the context description is based on each phonetic frame of the series of phonetic frames and at least some other phonetic frame of the series of phonetic frames; and a neural network, which can be trained, which generates an acoustic representation for each phonetic frame of the series of phonetic frames based on the context description, wherein training the neural network includes the steps of: a) inputting recorded audio messages; b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 34
Dependent Claim
26 words
The device of claim 29 further comprising: a synthesizer, operably connected to the neural network, that produces an audible signal in response to the acoustic representation.
Claim 35
Dependent Claim
570 words
A speech synthesizing device within a vehicular navigation system to generate an audible output to a driver of a vehicle comprising: a directional database consisting of a plurality of text streams; a text-to-phone processor, operably coupled to the directional database, wherein the text-to-phone processor translates a text stream of the plurality of text streams into a series of phonetic representations; a duration processor, operably coupled to the text-to-phone processor, wherein the duration processor generates duration data for the text stream; a pre-processor, wherein the pre-processor converts the series of phonetic representations and the duration data into a series of phonetic frames, wherein each phonetic frame of the series of phonetic frames is of a fixed duration and has a context description, and wherein the context description is based on the each phonetic frame of the series of phonetic frames and at least some other phonetic frame of the series of phonetic frames; a neural network, which can be trained, which generates an acoustic representation for a phonetic frame of the series of phonetic frames based on the context description, wherein training the neural network includes the steps of: a) inputting recorded audio messages; b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 36
Dependent Claim
570 words
A speech synthesizing device within a vehicular navigation system to generate an audible output to a driver of a vehicle comprising: a directional database consisting of a plurality of text streams; a text-to-phone processor, operably coupled to the directional database, wherein the text-to-phone processor translates a text stream of the plurality of text streams into a series of phonetic representations; a duration processor, operably coupled to the text-to-phone processor, wherein the duration processor generates duration data for the text stream; a pre-processor, wherein the pre-processor converts the series of phonetic representations and the duration data into a series of phonetic frames, wherein each phonetic frame of the series of phonetic frames is of a fixed duration and has a context description, and wherein the context description is based on the each phonetic frame of the series of phonetic frames and at least some other phonetic frame of the series of phonetic frames; a neural network, which can be trained, which generates an acoustic representation for a phonetic frame of the series of phonetic frames based on the context description, wherein training the neural network includes the steps of: a) inputting recorded audio messages; b) dividing the recorded audio messages into a series of audio frames, wherein each audio frame has a fixed duration; c) assigning, for each audio frame of the series of audio frames, a phonetic representation of a plurality of phonetic representations that include articulation characteristics; d) generating a context description of a plurality of context descriptions for each audio frame based on the phonetic representation of the each audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating syntactic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, generating phonetic boundary information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames, and generating a description of prominence of syntactic information based on the phonetic representation of the audio frame and the phonetic representation of at least some other audio frames of the series of audio frames; e) assigning, for the each audio frame, a target acoustic representation of a plurality of acoustic representations; f) training a feed-forward neural network with a recurrent input structure to associate an acoustic representation of the plurality of acoustic representations with the context description of the each audio frame, wherein the acoustic representation substantially matches the target acoustic representation; wherein upon receiving a text stream, converting the text stream into an audible signal utilizing the steps of: g) converting the text stream into a series of phonetic frames, wherein a phonetic frame of the series of phonetic frames includes one of the plurality of phonetic representations, and wherein a phonetic frame has the fixed duration; h) assigning one of the plurality of context descriptions to the phonetic frame based on the one of the plurality of phonetic representations and phonetic representations of at least some other phonetic frames of the series of phonetic frames; i) converting, by the neural network, the phonetic frame into one of the plurality of acoustic representations, based on the one of the plurality of context descriptions; and j) converting the one of the plurality of acoustic representations into an audible signal.
Claim 37
Dependent Claim
28 words
The vehicular navigation system of claim 31 further comprising: a synthesizer, operably connected to the neural network, that produces an audible signal in response to the acoustic representation.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2000-09-15 | 2001-03-17 | 2001-09-16 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2004-09-15 | 2005-03-17 | 2005-09-16 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2008-09-15 | 2009-03-17 | 2009-09-16 | $7700.00 | Window Closed / Lapsed |