Author: claimsbot

1. A behind-the-head mounted personal audio set for mounting to the crown area of a wearer’s head, said personal audio set comprising:
a substantially elongate headband portion defining a longitudinal centerline, said headband portion curved to encircle the crown of the wearer’s head; and,
an ear cup portion operably secured to the headband portion so as to be positioned substantially near one of the wearer’s ears, said ear cup portion having a center that substantially intersects said longitudinal centerline of the headband portion.

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 model adaptation device that makes a model approximate to a characteristic of an input characteristic amount, which is input data, to adapt the model to the input characteristic amount, said device comprising:
a model adaptation unit that performs model adaptation corresponding to each label from the input characteristic amount and a first supervised label sequence, which is the contents thereof, and outputs adapting characteristic information for the model adaptation;
a distance calculation unit that calculates a model-to-model distance between the adapting characteristic information and the model for each of the labels;
a detection unit that detects a label whose model-to-model distance exceeds a predetermined threshold value; and
a label generation unit that generates a second supervised label sequence containing at least one or more labels detected when one or more labels are obtained as an output of the detection unit.
2. A model adaptation device for model adaptation that makes an acoustic model used for voice recognition approximate to a characteristic of an input voice to adapt the acoustic model to a speaker of the input voice, said device comprising:
a text database that stores a plurality of sentences containing predetermined phonemes;
a sentence list that includes a plurality of sentences that describe the contents of the input voice;
an input unit to which the input voice is input;
a model adaptation unit that performs the model adaptation using the input voice and the sentence list and outputs adapting characteristic information, which is sufficient statistics for making the acoustic model approximate to the input voice;
a statistic database that stores the adapting characteristic information;
a distance calculation unit that calculates an acoustic distance between the adapting characteristic information and the acoustic model for each phoneme and outputs a distance value for each phoneme;
a phoneme detection unit that outputs, when there is a distance value, among the distance values, which is greater than a predetermined threshold value, the distance value exceeding the threshold value as a detection result; and
a label generation unit that searches the text database for a sentence containing a phoneme associated with the detection result and outputs the sentence extracted by the searching.
3. The model adaptation device according to claim 2, further comprising:
a determination unit that recognizes, when the label generation unit outputs a sentence after the searching, the sentence as a new sentence list, while informing of the fact that the sentence is not output from the label generation unit when the sentence is not output from the label generation unit;
a model update unit that acquires the adapting characteristic information from the statistic database after being informed by the determination unit of the fact that the sentence is not output, and applies the adapting characteristic information to the acoustic model to obtain an adapted acoustic model;
an output unit that outputs the adapted acoustic model; and
a sentence presentation unit that presents the sentence list and the new sentence list, wherein:
the model adaptation unit performs model adaptation again using the new sentence list and a voice input that is based on the new sentence list, and outputs the adapting characteristic information again;
the distance calculation unit calculates a distance between the acoustic model and the adapting characteristic information output again for each phoneme, and outputs a distance value of each phoneme again;
the phoneme detection unit outputs, when there is a distance value, among the distance values output again, which is greater than the threshold value, the distance value exceeding the threshold value as a detection result again; and
the label generation unit searches the text database for a sentence containing a phoneme associated with the detection result output again and outputs the sentence extracted by the searching.
4. The model adaptation device according to claim 2, wherein
the phoneme detection unit uses a different threshold value for each phoneme.
5. The model adaptation device according to claim 2, further comprising
a class database that stores information about classified phonemes or combinations of phonemes, wherein
the phoneme detection unit looks up the class database, and also outputs, when there is a distance value, among the distance values of each phoneme output from the distance calculation unit, which is greater than the threshold value, a phoneme belonging to the same class that the phoneme exceeding the threshold value belongs to as a detection result.
6. The model adaptation device according to claim 2, wherein the input voice includes a voice and data of an amount-of-characteristic sequence obtained by performing an acoustic analysis of the voice.
7. A model adaptation method that makes a model approximate to a characteristic of an input characteristic amount, which is input data, to adapt the model to the input characteristic amount, said method comprising:
a model adaptation step of performing model adaptation corresponding to each label from the input characteristic amount and a first supervised label sequence, which is the contents thereof, and outputting adapting characteristic information for the model adaptation;
a distance calculation step of calculating a model-to-model distance between the adapting characteristic information and the model for each of the labels;
a detection step of detecting a label whose model-to-model distance exceeds a predetermined threshold value; and
a label generation step of generating a second supervised label sequence containing at least one or more labels detected when one or more labels are obtained as an output of the detection step.
8. A model adaptation method for model adaptation that makes an acoustic model used for voice recognition approximate to a characteristic of an input voice to adapt the acoustic model to a speaker of the input voice, said method comprising:
an input step of inputting the input voice;
a model adaptation step of performing the model adaptation using the input voice and a sentence list including a plurality of sentences that describe the contents of the input voice, and outputting adapting characteristic information, which is sufficient statistics for making the acoustic model approximate to the input voice;
a step of storing the adapting characteristic information in a statistic database;
a distance calculation step of calculating an acoustic distance between the adapting characteristic information and the acoustic model for each phoneme, and outputting a distance value for each phoneme;
a phoneme detection step of outputting, when there is a distance value, among the distance values, which is greater than a predetermined threshold value, the distance value exceeding the threshold value as a detection result; and
a label generation step of searching a text database, which stores a plurality of sentences containing predetermined phonemes, for a sentence containing a phoneme associated with the detection result, and outputting the sentence extracted by the searching.
9. The model adaptation method according to claim 8, further comprising:
a determination step of recognizing, when the label generation step outputs a sentence after the searching, the sentence as a new sentence list, while informing of the fact that the sentence is not output from the label generation step when the sentence is not output from the label generation step;
a model update step of acquiring the adapting characteristic information from the statistic database after being informed by the determination step of the fact that the sentence is not output, and applying the adapting characteristic information to the acoustic model to obtain an adapted acoustic model;
an output step of outputting the adapted acoustic model; and
a sentence presentation step of presenting the sentence list and the new sentence list, wherein:
the model adaptation step performs model adaptation again using the new sentence list and a voice input that is based on the new sentence list, and outputs the adapting characteristic information again;
the distance calculation step calculates a distance between the acoustic model and the adapting characteristic information output again for each phoneme, and outputs a distance value of each phoneme again;
the phoneme detection step outputs, when there is a distance value, among the distance values output again, which is greater than the threshold value, the distance value exceeding the threshold value as a detection result again; and
the label generation step searches the text database for a sentence containing a phoneme associated with the detection result output again and outputs the sentence extracted by the searching.
10. The model adaptation method according to claim 8, wherein
the phoneme detection step uses a different threshold value for each phoneme.
11. The model adaptation method according to claim 8, further comprising
a step of storing in a class database information about classified phonemes or combinations of phonemes, wherein
the phoneme detection step looks up the class database, and also outputs, when there is a distance value, among the distance values of each phoneme output from the distance calculation step, which is greater than the threshold value, a phoneme belonging to the same class that the phoneme exceeding the threshold value belongs to as a detection result.
12. The model adaptation method according to claim 8, wherein the input voice includes a voice and data of an amount-of-characteristic sequence obtained by performing an acoustic analysis of the voice.
13. A non-transitory computer-readable medium including stored therein a model adaptation program that makes a model approximate to a characteristic of an input characteristic amount, which is input data, to adapt the model to the input characteristic amount, the model adaptation program causing a computer to execute:
a model adaptation process of performing model adaptation corresponding to each label from the input characteristic amount and a first supervised label sequence, which is the contents thereof, and outputting adapting characteristic information for the model adaptation;
a distance calculation process of calculating a model-to-model distance between the adapting characteristic information and the model for each of the labels;
a detection process of detecting a label whose model-to-model distance exceeds a predetermined threshold value; and
a label generation process of generating a second supervised label sequence containing at least one or more labels detected when one or more labels are obtained as an output of the detection process.
14. A non-transitory computer-readable medium including stored therein a model adaptation program for model adaptation that makes an acoustic model used for voice recognition approximate to a characteristic of an input voice to adapt the acoustic model to a speaker of the input voice, the model adaptation program causing a computer to execute:
an input process of inputting the input voice;
a model adaptation process of performing the model adaptation using the input voice and a sentence list including a plurality of sentences that describe the contents of the input voice, and outputting adapting characteristic information, which is sufficient statistics for making the acoustic model approximate to the input voice;
a process of storing the adapting characteristic information in a statistic database;
a distance calculation process of calculating an acoustic distance between the adapting characteristic information and the acoustic model for each phoneme, and outputting a distance value for each phoneme;
a phoneme detection process of outputting, when there is a distance value, among the distance values, which is greater than a predetermined threshold value, the distance value exceeding the threshold value as a detection result; and
a label generation process of searching a text database, which stores a plurality of sentences containing predetermined phonemes, for a sentence containing a phoneme associated with the detection result, and outputting the sentence extracted by the searching.
15. The non-transitory computer-readable medium according to claim 14, wherein the model adaptation program further causes a computer to execute:
a determination process of recognizing, when the label generation process outputs a sentence after the searching, the sentence as a new sentence list, while informing of the fact that the sentence is not output from the label generation process when the sentence is not output from the label generation process;
a model update process of acquiring the adapting characteristic information from the statistic database after being informed by the determination process of the fact that the sentence is not output, and applying the adapting characteristic information to the acoustic model to obtain an adapted acoustic model;
an output process of outputting the adapted acoustic model; and
a sentence presentation process of presenting the sentence list and the new sentence list, wherein:
the model adaptation process performs model adaptation again using the new sentence list and a voice input that is based on the new sentence list, and outputs the adapting characteristic information again;
the distance calculation process calculates a distance between the acoustic model and the adapting characteristic information output again for each phoneme, and outputs a distance value of each phoneme again;
the phoneme detection process outputs, when there is a distance value, among the distance values output again, which is greater than the threshold value, the distance value exceeding the threshold value as a detection result again; and
the label generation process searches the text database for a sentence containing a phoneme associated with the detection result output again and outputs the sentence extracted by the searching.
16. The non-transitory computer-readable medium according to claim 14, wherein the phoneme detection process uses a different threshold value for each phoneme.
17. The non-transitory computer-readable medium according to claim 14, wherein the model adaptation program further causes a computer to execute
a process of storing in a class database information about classified phonemes or combinations of phonemes, wherein
the phoneme detection process looks up the class database, and also outputs, when there is a distance value, among the distance values of each phoneme output from the distance calculation process, which is greater than the threshold value, a phoneme belonging to the same class that the phoneme exceeding the threshold value belongs to as a detection result.
18. The non-transitory computer-readable medium according to claim 14, wherein the input voice includes a voice and data of an amount-of-characteristic sequence obtained by performing an acoustic analysis of the voice.

1. A method for mapping the open fractures in a subsurface formation using two compressional seismic waves (signals) transmitted from the surface into the subsurface formations and recording the reflected signals from the formation boundaries by using multiple receivers located on the surface, the receivers laid out in a pattern to provide three dimensional (3-D) seismic coverage; the 3-D seismic coverage designed to provide a predetermined range of sourcereceiver offset distances and azimuthal angle distribution. One of the transmitted signals is a swept frequency seismic signal and the other a mono-frequency seismic signal, both signals transmitted from the same surface location. Both the seismic signals are synchronized with each other and have the same start-up time and the duration of transmission period. The frequencies of both signals are pre-selected.
2. The method in claim 1 further comprising:
Recording the 3-D seismic data, with both the surface sources transmitting their signals simultaneously, in a manner that the recorded data will provide adequate and required subsurface coverage of the particular subsurface formation that is to be mapped for open fractures, data recorded using the two surface vibratory sources that transmit the swept frequency and the mono-frequency signals, both sources moving together in a source pattern that is designed to provide the necessary subsurface coverage of the fractured formation to be mapped.
3. The method in claim 1 further comprising:
Since there are two sets of data that have been superimposed due to two seismic sources transmitting two different seismic signals simultaneously, separating one set of data by cross-correlating the recorded data with the swept frequency signal, and using this cross-correlated data for conventional and standard 3-D seismic reflection processing.
4. The method in claim 1 further comprising:
Synthetically computer generating two additional swept frequency signals, one swept frequency signal by the frequency addition of the mono-frequency signal to the swept frequency signal, and the second swept signal generated by the frequency subtraction of the mono-frequency signal from the swept frequency signal, and cross-correlating the recorded data with these two synthetically generated sweep signals, in this manner generating two additional sets of cross-correlated seismic reflection data that have new frequenciesnot transmitted from the surface, but generated when the transmitted seismic signals nonlinearly interact with each other as they intersect the fractures at right angles. There will be no nonlinear interaction and no new frequencies will be generated when the two transmitted signals do not intersect the fractures, either due to the absence of the fractures or due to their propagation in the same plane as the fractures so the transmission path is parallel to the fractures.
5. The method in claim 1 further comprising:
The said cross-correlated data generated using synthetic sweeps analyzed to detect the fractures’ location and orientation, this cross-correlated data represents and consists of the full volume of 3-D seismic information that has a full range of sourcereceiver offset distances and sourcereceiver azimuthal angle distribution, this data to be sorted according to different sourcereceiver azimuthal angles, so that it is sliced as two dimensional (2-D) seismic lines, each representing a particular sourcereceiver azimuthal angle, these 2-D seismic lines processed for subsurface reflection imaging, using processing parameters that have been established by processing the standard 3-D data set that was created by cross-correlating with the swept signal that was transmitted from the surface source.
6. The method in claim 1 further comprising:
Identifying the sourcereceiver azimuthal angle that provides the most coherent reflected image of the fractured subsurface formation in the processed results of the 2-D seismic lines that have been sliced according to different azimuthal angles, this sourcereceiver azimuthal angle intersects the fractures at right angles, use this azimuthal angle information to determine the orientation of the open fractures, further confirm the results by analysis of the reflection image of the fractured formation at the sourcereceiver azimuthal angle that is parallel to the fractures, there should be no reflection image of the said formation, the location of the open fractures is defined by the reflection image of the said fractured formation, the presence of the reflected image indicates the presence of fractures, and the common mid-point of the imaged reflection identifies the subsurface location.

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 current limiting system for current control of light fixtures comprising:
a light source;
a sensor adapted to sense the electrical current flow passing to said light source and to communicate an input signal associated with the sensed current flow;
a variable switch adapted to regulate the current flow to said light source; and
a controller in communication with said sensor and said variable switch, said controller being adapted to monitor said input signal from said sensor and being adapted to compare said input signal to a predetermined current value, said controller actuates said variable switch to reduce the current passing to said light source upon said input signal associated with the sensed current exceeding said predetermined current value.
2. The system of claim 1, wherein the controller is further structured to be in communication with and at least partially operated via a remote control.
3. The system of claim 1 wherein the variable switch comprises at least one of a triac, a diode, or a semi-conducting switch device.
4. A method of limiting current to a light fixture comprising the steps of:
(A) providing a light source;
(B) providing a sensor adapted to sense the electrical current passing to the light source and to communicate an input signal associated with the sensed current flow;
(C) providing a variable switch adapted to regulate the current flow to said light source; and
(D) providing a controller in communication with the sensor and the variable switch;
(E) monitoring the input signal from the sensor with the controller and comparing the input signal to a predetermined current value, and
(F) actuating the variable switch to reduce the current passing to the light source upon the input signal associated with the sensed current exceeding the predetermined current value.
5. The method of claim 4 wherein the controller is further structured to be in communication with and at least partially operated via a remote control.
6. The method of claim 4 wherein the variable switch comprises at least one of a triac, a diode, or a semi-conducting switch device.