Speech recognizer

Claim 2

In a speech recognizer which operates to match an unknown speech segment comprising a fine sequence of frames with model segments represented by respective fine sequences of states, the respective fine sequences of the unknown segment an a model segment together defining a fine matrix; a method of determining a good alignment of the unknown segment with a model segment, said method comprising: preparing respective coarse sequences representing said unknown speech segment and said model segment thereby to obtain a respective coarse matrix; determining a best alignment of said coarse sequences thereby determining a coarse path through said coarse matrix; overlaying said coarse path on said fine matrix and determining which fine matrix locations lie within a preselected metric of said coarse path thereby defining a corridor of possible paths through said fine matrix; and calculating only transitions within said corridor, determining an alignment of said unknown segment with said model segment.

Claim 3

The method according to claim 1 wherein said coarse sequences are obtained by subsampling the respective fine sequences, skipping a predetermined number between subsamples.

Claim 4

The method according to claim 2 wherein said coarse sequences include the first and last items from the respective fine sequences.

Claim 5

The method according to claim 1 wherein the best alignment of said coarse sequences is determined by Viterbi decoding.

Claim 6

The method according to claim 4 wherein the alignment of fine sequences is obtained by Viterbi decoding constrained to transitions within said corridor.

Claim 7

The method according to claim 1 wherein said predetermined metric selects as being within said corridor only fine matrix locations which are within a predetermined number of frames of said coarse path.

Claim 8

In a speech recognizer which operates to match an unknown speech segment comprising a fine sequence of multi-dimensional spectral frames with model segments also represented by a respective fine sequences of states, the respective fine sequences of the unknown segment and a model segment together defining a fine matrix which encompasses all possible alignments of the unknown segment fine sequence with the respective model segment fine sequence; a method of determining a good alignment of the unknown segment fine sequence with a model segment fine sequence, said method comprising: preparing respective coarse sequences representing said unknown speech segment and said model segment thereby to obtain a coarse matrix representing possible alignments of said unknown and model segments; determining a best alignment of said coarse sequences thereby determining a coarse path through said coarse matrix; overlaying said coarse path on said fine matrix and interpolating between the locations defining said coarse path thereby to determine a base path across said fine matrix; determining which fine matrix locations lie within a preselected metric of said coarse path thereby defining a corridor comprising a limited number of possible paths through said fine matrix; and calculating only transitions within said corridor, determining a best alignment of said unknown segment fine sequence with said model segment fine sequence.

Claim 9

In a speech recognizer which operates to match an unknown speech segment comprising a fine sequence of multi-dimensional spectral frames with model segments also represented by a respective fine sequences of states, the respective fine sequences of the unknown segment and a model segment together defining a fine matrix which encompasses all possible alignments of the unknown segment fine sequence with the respective model segment fine sequence; a method of determining a good alignment of the unknown segment fine sequence with a model segment fine sequence, said method comprising: preparing respective coarse sequences representing said unknown speech segment and said model segment thereby to obtain a coarse matrix representing possible alignments of said unknown and model segments; determining a best alignment of said coarse sequences thereby determining a coarse path through said coarse matrix; overlaying said coarse path on said fine matrix and interpolating between the locations defining said coarse path thereby to determine a base path across said fine matrix; determining which fine matrix locations lie within a preselected metric of said coarse path thereby defining a corridor comprising a limited number of possible paths through said fine matrix; and calculating only transitions within said corridor, determining a best alignment of said unknown segment fine sequence with said model segment fine sequence.

Claim 10

The method as set forth in claim 7 wherein said coarse sequences are obtained by subsampling the respective fine sequences.

Claim 11

The method as set forth in claim 8 wherein said predetermined metric selects as being within said corridor only fine matrix locations which are within a preselected number of frames of said base path.

Claim 12

In a speech recognizer which operates to match an unknown speech segment comprising a fine sequence of frames with model segments represented by respective fine sequences of states, the respective fine sequences of the unknown segment and a model segment together defining a fine matrix which encompasses all possible alignments of the unknown segment with the respective model segment; a method of determining a good alignment of the unknown segment with a model segment, said method comprising: subsampling the respective fine sequences thereby to obtain respective coarse sequences representing said unknown speech segment and said model segment and thereby to define a coarse matrix representing possible alignments of said unknown and model segments; determining a best alignment of said coarse sequences thereby determining a coarse path through said coarse matrix; overlaying said coarse path on said fine matrix and interpolating between the locations defining said coarse path thereby to determine a base path across said fine matrix; determining which fine matrix locations lie within a preselected number of locations of said base path thereby defining a corridor of possible paths through said fine matrix; and calculating only transitions within said corridor, determining a best fine alignment of said unknown segment with said model segment.

Claim 13

In a speech recognizer which operates to match an unknown speech segment comprising a fine sequence of frames with model segments represented by respective fine sequences of states, the respective fine sequences of the unknown segment and a model segment together defining a fine matrix which encompasses all possible alignments of the unknown segment with the respective model segment; a method of determining a good alignment of the unknown segment with a model segment, said method comprising: subsampling the respective fine sequences thereby to obtain respective coarse sequences representing said unknown speech segment and said model segment and thereby to define a coarse matrix representing possible alignments of said unknown and model segments; determining a best alignment of said coarse sequences thereby determining a coarse path through said coarse matrix; overlaying said coarse path on said fine matrix and interpolating between the locations defining said coarse path thereby to determine a base path across said fine matrix; determining which fine matrix locations lie within a preselected number of locations of said base path thereby defining a corridor of possible paths through said fine matrix; and calculating only transitions within said corridor, determining a best fine alignment of said unknown segment with said model segment.