Systems and methods may provide for partitioning a plurality of training samples into a first sequential list of centroids, removing one or more repeating centroids in the first sequential list of centroids to obtain a first reduced list of centroids and generating a set of Hidden Markov Model (HMM) parameters based on the first reduced list of centroids. Additionally, a plurality of detection samples may be partitioned into a second sequential list of centroids, wherein one or more repeating centroids in the second sequential list of centroids may be removed to obtain a second reduced list of centroids. The second reduced list of centroids may be used to determine a match probability for the plurality of detection samples against the set of HMM parameters. In one example, the reduced lists of centroids lack temporal variability.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. An apparatus to process training samples, comprising: a computing device including a sensor to collect a plurality of training samples selected from human motion samples or human verbal samples, the computing device including: a partition module to partition the plurality of training samples into a sequential list of centroids; a filter to remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and a parameter module to generate a set of parameters based on the reduced list of centroids. 3. The apparatus of claim 2, wherein the reduced list of centroids is to lack temporal variability. 4. The apparatus of claim 2, wherein the sequential list of centroids is to be associated with a set of clusters having a one-to-one mapping to states in a Hidden Markov Model (HMM). 5. The apparatus of claim 2, wherein the partition module includes: a cluster unit to determine a set of clusters for the plurality of training samples; a centroid unit to determine a set of cluster centroids corresponding to the set of clusters; and an assignment unit to assign each training sample in the plurality of training samples to a centroid in the set of cluster centroids. 6. A method to process training samples, comprising: partitioning a plurality of training samples selected from human movement samples or human verbal samples into a sequential list of centroids; removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and generating a set of parameters based on the reduced list of centroids. 7. The method of claim 6, wherein the reduced list of centroids lacks temporal variability. 8. The method of claim 6, wherein sequential list of centroids is associated with a set of clusters having a one-to-one mapping to states in a Hidden Markov Model (HMM). 9. The method of claim 6, wherein partitioning the plurality of training samples includes: determining a set of clusters for the plurality of training samples; determining a set of cluster centroids corresponding to the set of clusters; and assigning each training sample in the plurality of training samples to a centroid in the set of cluster centroids. 10. A method to process detection samples, comprising: partitioning a plurality of detection samples selected from human movement samples or human verbal samples into a sequential list of centroids; removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and using the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of parameters associated with a training session. 11. The method of claim 10, wherein the reduced list of centroids lacks temporal variability. 12. The method of claim 10, wherein the sequential list of centroids is associated with a set of clusters having a one-to-one mapping to states in a Hidden Markov Model (HMM). 13. The method of claim 10, wherein using the list of centroids to determine the match probability includes applying a sliding window to the reduced list of centroids, and wherein the sliding window has a fixed width that equals a number of states in a Hidden Markov Model (HMM). 14. At least one non-transitory computer readable storage medium comprising a set of instructions which, if executed by a computing device, cause the computing device to: partition a plurality of detection samples selected from human movement samples and human verbal samples into a sequential list of centroids; remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and use the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of parameters associated with a training session. 15. The at least one computer readable storage medium of claim 14, wherein the reduced list of centroids is to lack temporal variability. 16. The at least one computer readable storage medium of claim 14, wherein the sequential list of centroids is to be associated with a set of clusters having a one-to-one mapping to states in a Hidden Markov Model (HMM). 17. The at least one computer readable storage medium of claim 14, wherein the instructions, if executed, cause a computing device to apply a sliding window to the reduced list of centroids to use the list to determine the match probability, and wherein the sliding window is to have a fixed width that equals a number of states in a Hidden Markov Model (HMM).