1. A method of packaging a power light emitting diode (LED), comprising:
providing a printed circuit board (PCB) wherein first and second copper (Cu) thin films are formed on both faces of the PCB respectively;
forming a single upper opening through an entire thickness of the first Cu thin film and an partial thickness of the PCB;
forming a plurality of lower openings, each lower opening extending vertically from the upper opening to the second Cu thin film;
forming solder pads on the first Cu thin film;
filing a cream solder in the upper opening and the plurality of lower openings so as to be in-plane with the solder pads;
mounting a power LED on the PCB so that lead frames of the LED are aligned with the solder pads and a heat-discharge region of the LED is aligned with the cream solder; and
soldering the cream solder.
2. The method of claim 1, wherein forming the upper andor plurality of lower openings are carried out in a punching manner.
3. The method of claim 1, further comprising adhering the second Cu thin film to an aluminum heat-discharge structure.
4. The method of claim 1, wherein the upper andor plurality of lower openings are formed during providing the PCB.
5. A power light emitting diode (LED) package, comprising:
a first copper thin film;
a printed circuit board (PCB) formed on the first copper thin film;
a second copper thin film formed on the PCB;
a solder vertically formed through the second copper thin film and the PCB;
solder pads formed on the second copper thin film; and
a power LED disposed on the solder pads,
wherein lead frames of the LED are in contact with the solder pads and a heat-discharge region of the LED is aligned with the solder,
wherein the solder is in-plane with the solder pads,
wherein the solder is defined into a plurality of sub-solders in a lower portion thereof, and
wherein a resin isolation is formed between neighboring sub-solders.
6. The package of claim 5, wherein a material of the resin isolation is the same as a material of the PCB.
7. The package of claim 5, wherein the resin isolation is made of FR4.
8. The package of claim 5, wherein the PCB is made of FR4.
9. The package of claim 5, wherein the solder is made of a Pb-free solder.
10. The package of claim 5, further comprising an aluminum heat-discharge structure attached to the first copper thin film.
11. The package of claim 5, wherein the sub-solders are in contact with the first copper thin film.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for speech dialog, comprising:
receiving an input speech phrase that includes an instantiated variable;
extracting pitch and voicing characteristics for the instantiated variable;
performing voice recognition of the instantiated variable to determine a most likely set of recognition acoustic states;
converting the most likely set of recognition acoustic states to a most likely set of synthesis acoustic states; and
generating a synthesized value of the instantiated variable using the most likely set of synthesis acoustic states and the extracted pitch and voicing characteristics.
2. The method for speech dialog according to claim 1, wherein said performing voice recognition of the instantiated variable comprises:
extracting acoustic characteristics in the form of recognition feature vectors of the instantiated variable; and
comparing the extracted acoustic characteristics to a mathematical model of stored lookup values to determine a most likely set of the extracted recognition feature vectors representing the most likely set of recognition acoustic states.
3. The method for speech dialog according to claim 2, said converting further comprising:
deriving recognition feature vectors from training speech uttered by at least one speaker;
deriving synthesis feature vectors from the training speech uttered by the at least one speaker, each of the derived synthesis vectors corresponding on a one-to-one basis to one of the derived recognition vectors;
mapping a plurality of subsets of the derived recognition feature vectors to a most likely set of synthesis feature vectors, and
for each of the most likely set of recognition feature vectors:
determining the probability that the most likely recognition feature vector belongs in one or more of the subsets; and
selecting a most likely synthesis feature vector based on the determined probability and the mapping.
4. The method for speech dialog according to claim 3, wherein the extracted and derived recognition feature vectors comprise Mel-frequency cepstrum coefficients and the extracted and derived synthesis feature vectors comprise linear prediction coding compatible coefficients.
5. The method for speech dialog according to claim 1, wherein said generating the synthesized value of the instantiated variable is performed when a metric of the most likely set of recognition acoustic states meets a criterion, and further comprising presenting an acoustically stored out-of-vocabulary response phrase when the metric of the most likely set of recognition acoustic states fails to meet the criterion.
6. The method for speech dialog according to claim 1, wherein the speech phrase further includes a non-variable segment that is associated with the instantiated variable, further comprising:
performing voice recognition of the non-variable segment; and
presenting an acoustically stored response phrase based on the recognized non-variable segment.
7. The method for speech dialog according to claim 3, wherein said mapping further comprises
creating the subsets of extracted recognition feature vectors through vector quantization;
establishing a vector quantization table comprising a plurality of entries, each of the entries comprising a centroid for a different one of the subsets; and
determining the most likely synthesis feature vector for each of the entries, comprising:
selecting an appropriate one of the derived recognition feature vectors from the subset corresponding to each entry; and
associating the entry with the derived synthesis feature vector that corresponds to the appropriate recognition feature vector on a one-to-one basis.
8. The method for speech dialog according to claim 7, wherein the selected appropriate one of the derived recognition feature vectors is the derived recognition feature vector of the subset that is closest to the centroid.
9. The method for speech dialog according to claim 3, wherein said mapping further comprises:
modeling the derived recognition feature vectors such that each of the subsets is a statistical distribution characterized by a mean vector, a covariance matrix and a non-negative weight; and
determining the most likely synthesis feature vector that corresponds to each of the most likely set of recognition feature vectors based on the probability that each of the set of most likely recognition feature vectors is in any one of the subsets.
10. The method for speech dialog according to claim 9, wherein said determining the most likely synthesis feature vector further comprises:
computing a weight for each of the set of most likely recognition feature vectors, each weight corresponding to the probability the most likely synthesis feature vector is in that one of the subsets; and
applying the computed weights for each of the subsets to the derived synthesis feature vectors each of which corresponds to the derived recognition feature vectors comprising each of the subsets to obtain the converted most likely synthesis feature vector.
11. An electronic device for speech dialog, comprising:
means for receiving an input speech phrase that includes an instantiated variable;
means for extracting pitch and voicing characteristics for the instantiated variable;
means for performing voice recognition of the instantiated variable to determine a most likely set of recognition acoustic states;
means for converting the most likely set of recognition acoustic states to a most likely set of synthesis acoustic states; and
means for generating a synthesized value of the instantiated variable using the most likely set of synthesis acoustic states and the extracted pitch and voicing characteristics.
12. The electronic device for speech dialog according to claim 11, wherein said means for performing voice recognition of the instantiated variable comprises:
means for extracting acoustic characteristics in the form of recognition feature vectors of the instantiated variable; and
means for comparing the extracted acoustic characteristics to a mathematical model of stored lookup values to determine a most likely set of the extracted recognition feature vectors representing the most likely set of recognition acoustic states.
13. The electronic device for speech dialog according to claim 12, said means for converting further comprising:
means for deriving recognition feature vectors from training speech uttered by at least one speaker;
means for deriving synthesis feature vectors from the training speech uttered by the at least one speaker, each of the derived synthesis vectors corresponding on a one-to-one basis to one of the derived recognition vectors;
means for mapping a plurality of subsets of the derived recognition feature vectors to a most likely set of synthesis feature vectors, and
means for determining the probability that each of the most likely set of recognition feature vectors belongs in one or more of the subsets; and
means for selecting a most likely synthesis feature vector for each of the most likely set of recognition feature vectors based on the determined probability and the mapping.
14. The electronic device for speech dialog according to claim 13, wherein said means for mapping further comprises
means for creating the subsets of extracted recognition feature vectors through vector quantization;
means for establishing a vector quantization table comprising a plurality of entries, each of the entries comprising a centroid for a different one of the subsets; and
means for determining the most likely synthesis feature vector for each of the entries, comprising:
means for selecting an appropriate one of the derived recognition feature vectors from the subset corresponding to each entry; and
means for associating the entry with the derived synthesis feature vector that corresponds to the appropriate recognition feature vector on a one-to-one basis.
15. The electronic device for speech dialog according to claim 14, wherein the selected appropriate one of the derived recognition feature vectors is the derived recognition feature vector of the subset that is closest to the centroid.
16. The electronic device for speech dialog according to claim 13, wherein said means for mapping further comprises:
means for modeling the derived recognition feature vectors such that each of the subsets is a statistical distribution characterized by a mean vector, a covariance matrix and a non-negative weight; and
means for determining the most likely synthesis feature vector that corresponds to each of the most likely set of recognition feature vectors based on the probability that each of the set of recognition feature vectors is in any one of the subsets.
17. The electronic device for speech dialog according to claim 11, wherein said means for determining the most likely synthesis feature vector further comprises:
means for computing a weight for each of the set of most likely recognition feature vectors, each weight corresponding to the probability the most likely synthesis feature vector is in that one of the subsets; and
means for applying the computed weights for each of the subsets to the derived synthesis feature vectors each of which corresponds to the derived recognition feature vectors comprising each of the subsets to obtain the converted most likely synthesis feature vector.
18. A media that includes a set of stored program instructions, comprising:
a function for receiving an input speech phrase that includes an instantiated variable;
a function for extracting pitch and voicing characteristics for the instantiated variable;
a function for performing voice recognition of the instantiated variable to determine a most likely set of recognition acoustic states;
a function for converting the most likely set of recognition acoustic states to a most likely set of synthesis acoustic states; and
a function for generating a synthesized value of the instantiated variable using the most likely set of synthesis acoustic states and the extracted pitch and voicing characteristics.
19. The media that includes a set of stored program instructions according to claim 18, wherein said function for performing voice recognition of the instantiated variable comprises:
a function for extracting acoustic characteristics in the form of recognition feature vectors of the instantiated variable; and
a function for comparing the extracted acoustic characteristics to a mathematical model of stored lookup values to determine a most likely set of the extracted recognition feature vectors representing the most likely set of recognition acoustic states.
20. The media that includes a set of stored program instructions according to claim 19, said function for mapping further comprising:
a function for modeling the derived recognition feature vectors such that each of the subsets is a statistical distribution characterized by a mean vector, a covariance matrix and a non-negative weight; and
a function for determining the most likely synthesis feature vector that corresponds to each of the most likely set of recognition feature vectors based on the probability that each of the set of most likely recognition feature vectors is in any one of the subsets.