1. A speech recognition method adapted for an electronic apparatus, the speech recognition method comprising:
obtaining a feature vector from a speech signal;
inputting the feature vector to a plurality of speech recognition modules and obtaining a plurality of string probabilities and a plurality of candidate strings from the plurality of speech recognition modules respectively, wherein the plurality of speech recognition modules respectively correspond to a plurality of languages; and
selecting the candidate string corresponding to the largest one of the plurality of string probabilities as a recognition result of the speech signal.
2. The speech recognition method according to claim 1, wherein the step of inputting the feature vector to the plurality of speech recognition modules and obtaining the plurality of string probabilities and the plurality of candidate strings from the plurality of speech recognition modules respectively comprises:
inputting the feature vector to an acoustic model of each of the plurality of speech recognition modules and obtaining a candidate phrase corresponding to each of the plurality of languages based on a corresponding acoustic dictionary; and
inputting the candidate phrase to a language model of each of the plurality of speech recognition modules to obtain the plurality of candidate strings and the plurality of string probabilities corresponding to the plurality of languages.
3. The speech recognition method according to claim 2, further comprising:
obtaining the acoustic model and the acoustic dictionary through training based on a speech database corresponding to each of the plurality of languages; and
obtaining the language model through training based on a text corpus corresponding to each of the plurality of languages.
4. The speech recognition method according to claim 1, further comprising:
receiving the speech signal by an input unit.
5. The speech recognition method according to claim 1, wherein the step of obtaining the feature vector from the speech signal comprises:
dividing the speech signal into a plurality of frames; and
obtaining a plurality of feature parameters from each of the plurality of frames to obtain the feature vector.
6. An electronic apparatus, comprising:
a processing unit;
a storage unit coupled to the processing unit and storing a plurality of code snippets to be executed by the processing unit; and
an input unit coupled to the processing unit and receiving a speech signal;
wherein the processing unit drives a plurality of speech recognition modules corresponding to a plurality of languages by the code snippets and executes: obtaining a feature vector from the speech signal and inputting the feature vector to the plurality of speech recognition modules to obtain a plurality of string probabilities and a plurality of candidate strings from the plurality of speech recognition modules respectively; and
selecting the candidate string corresponding to the largest one of the plurality of string probabilities.
7. The electronic apparatus according to claim 6, wherein the processing unit inputs the feature vector to an acoustic model of each of the plurality of speech recognition modules and obtains a candidate phrase corresponding to each of the plurality of languages based on a corresponding acoustic dictionary; and inputs the candidate phrase to a language model of each of the plurality of speech recognition modules to obtain the plurality of candidate strings and the plurality of string probabilities corresponding to the plurality of languages.
8. The electronic apparatus according to claim 7, wherein the processing unit obtains the acoustic model and the acoustic dictionary through training based on a speech database corresponding to each of the plurality of languages; and obtains the language model through training based on a text corpus corresponding to each of the plurality of languages.
9. The electronic apparatus according to claim 6, wherein the processing unit drives a feature extracting module by the code snippets and executes: dividing the speech signal into a plurality of frames and obtaining a plurality of feature parameters from each of the plurality of frames to obtain the feature vector.
10. The electronic apparatus according to claim 6, further comprising:
an output unit outputting the candidate string corresponding to the largest one of the plurality of string probabilities.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
What is claimed is:
1. A measurement system comprising:
at least one stationary array of sensors at a first location to produce a first array of measurement outputs;
at least one scanning sensor at a second location to produce a second array of measurement outputs; and
means for synthesizing an array of measurement outputs by fusing the first and second arrays of measurement outputs.
2. The measurement system of claim 1, wherein the stationary and scanning measurements are compared and reconciled so that the measurements made by a plurality of sensors are attributed to the same point on material that is being measured.
3. The measurement system of claim 1, wherein the measurements comprise time stamp information, cross direction coordinates, machine direction coordinates, and at least one of machine direction odometer or velocity information.
4. The measurement system of claim 1, wherein the synthetic measurement is provided by computing an offset using a recursive least mean square algorithm.
5. The measurement system of claim 4, wherein the recursive least mean square algorithm is a Kalman filter.
6. The measurement system of claim 5, wherein the Kalman filter output data is used to compensate for different sensor inputs and bias errors.
7. The measurement system of claim 5, wherein the Kalman filter output data is used to compensate for the temporal variations in the biases of an array of stationary sensors.
8. The measurement system of claim 1, wherein data measurements from stationary and scanning sensors are compared by a Kalman filter and an offset compensation for the sensor measurement drift is calculated.
9. A method for fusing data measurements obtained from plural locations in a product manufacturing process comprising:
measuring a variable of at least one of the product properties and the process with at least one stationary sensor at a first location in the manufacturing process to produce a first output;
measuring the variable of at least one of the product properties and the process with a scanning sensor at a second location in the manufacturing process to produce a second output; and
producing a synthetic measurement by fusing the first and second outputs.
10. The method of claim 9, wherein the stationary and scanning measurements are compared and reconciled so that the measurements made by a plurality of sensors are attributed to the same spot on material that is being measured.
11. The method of claim 10, wherein the measurements comprise time stamp information, cross direction coordinates, machine direction coordinates, and at least one of machine direction odometer or velocity information.
12. The method of claim 9, wherein the synthetic measurement is provided using an offset computed by a recursive algorithm.
13. The method of claim 12, wherein the recursive algorithm is a Kalman filter.
14. The method of claim 13, wherein the Kalman filter uses different sensor inputs and computes bias errors.
15. The method of claim 13, wherein the Kalman filter computes the temporal variations in the biases of an array of stationary sensors.
16. The method of claim 9, wherein data measurements from stationary and scanning sensors are compared by a Kalman filter and an offset compensation for the sensor measurement drift is calculated.