On-demand language processing system and method

Claim 2

In a speech processing system wherein predetermined linguistic information is represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, a method for input speech evaluation by on-demand expansion of a selected portion of at least one of said network models comprising the steps of: formulating said at least one of said network models as a finite state transducer, said transducer representing said mapping function between said at least one network model and an adjacent one of said cascaded network models; selecting a state in a one of said at least one transducers having a correspondence to said selected portion of said at least one network model; composing said one of said at least one transducers with a next successively higher level of said network models to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said at least one network model; iteratively repeating said selecting step and said composing step for each successively higher level network model representing said selected portion; and evaluating an input speech signal against a candidate speech segment provided by one of said network models.

Claim 3

The method for on-demand expansion of claim 1 wherein at least one of said levels of said network cascade is represented in terms of context dependent units.

Claim 4

The method for on-demand expansion of claim 1 wherein said selecting step and said composing step are implemented as a generalized state machine C, said machine being created as a composition of a generalized state machine A, corresponding to said one of said at least one transducers, with a generalized state machine B, corresponding to said next successively higher level of said network cascade.

Claim 5

The method for on-demand expansion of claim 3 wherein said generalized state machine C supports the functions: C.start:=returns A.start paired with B.start C.final((s 1 ,s 2 )):=for state s 1 ,s 2 , returns A.final(s 1 ) Ù B.final(s 2 ) C.arcs((s 1 ,s 2 )):=for state s 1 ,s 2 , returns Merge(A.arcs(s 1 ), B.arcs(s 2 )) where merged arcs are defined as: (label 1 , label 3 , x+y, (next state 1 , next state 2 )) Î Merge(A.arcs(s 1 ), B.arcs(s 2 )) iff (label 1 , label 2 , x, next state 1 ) Î A.arcs(s 1 ) and (label 2 , label 3 , y, next state 2 ) Î B.arcs(s 2 ); where, using M to represent any of generalized state machines C, A or B: M.start returns, on request, the start state for machine M; M.final(state) represents probability of accepting at a given state; and M.arcs(state) returns, for a given state, transitions (a 1 , a 2 , . . . a N ) leaving the given state, where a i =(input label, output label, weight & next state).

Claim 6

The method for on-demand expansion of claim 1 further including a step of storing at least a part of said on-demand expansion of said selected portion of said network cascade in a storage cache.

Claim 7

The method for on-demand expansion of claim 5 wherein said storage cache is implemented as a generalized state machine of the form: N.start:=M.start N.final(state):=M.final(state) N.arcs(state):=M.arcs(state) where: M represents an input machine and N represents said cache machine; M.start returns, on request, the start state for machine M; M.final(state) represents probability of accepting at a given state; and M.arcs(state) returns, for a given state, transitions (a 1 , a 2 , . . . a N ) leaving the given state, where a i =(input label, output label, weight & next state); and corresponding functions of generalized state machine N are equivalently defined.

Claim 8

The method for on-demand expansion of claim 1 wherein said selected portion of said network cascade corresponds to an unknown speech fragment of interest.

Claim 9

The method for on-demand expansion of claim 1 further including the step of inserting a filter in said composition of said one of said at least one transducers with said next successively higher level, wherein said filter operates to remove redundant paths occurring between a pair of states in said composition.

Claim 10

The method for on-demand expansion of claim 8 wherein said filter is manifested as a finite state transducer.

Claim 11

A speech processing system operable to provide on-demand expansion of a selected portion of a linguistic model, wherein predetermined linguistic information established for training said system is represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, said system comprising: decoding means for evaluating input speech signals against a candidate speech segment provided by one of said network models; a finite state transducer operable to represent said mapping function between at least one of said network models and an adjacent one of said cascaded network models; means for selecting a state in said transducer having a correspondence to said selected portion of said network model; means for composing said transducer with a next successively higher level of said network model to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said network model; and means for iteratively applying said transducer and said composing means to successively higher level network models representing said selected portion.

Claim 12

A speech processing system operable to provide on-demand expansion of a selected portion of a linguistic model, wherein predetermined linguistic information established for training said system is represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, said system comprising: decoding means for evaluating input speech signals against a candidate speech segment provided by one of said network models; a finite state transducer operable to represent said mapping function between at least one of said network models and an adjacent one of said cascaded network models; means for selecting a state in said transducer having a correspondence to said selected portion of said network model; means for composing said transducer with a next successively higher level of said network model to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said network model; and means for iteratively applying said transducer and said composing means to successively higher level network models representing said selected portion.

Claim 13

The speech processing system of claim 10 further characterized in that at least one of said levels of said network cascade is represented in terms of context dependent units.

Claim 14

The speech processing system of claim 10 further characterized in that said selecting step and said composing step are implemented as a generalized state machine C, said machine being created as a composition of a generalized state machine A, corresponding to said one of said at least one transducers, with a generalized state machine B, corresponding to said next successively higher level of said network cascade.

Claim 15

The speech processing system of claim 12 wherein said generalized state machine C supports the functions: C.start:=returns A.start paired with B.start C.final((s 1 ,s 2 )):=for state s 1 ,s 2 , returns A.final(s 1 ) Ù B.final(s 2 ) C.arcs((s 1 ,s 2 )):=for state s 1 ,s 2 , returns Merge(A.arcs(s 1 ), B.arcs(s 2 )) where merged arcs are defined as: (label 1 , label 3 , x+y, (next state 1 , next state 2 )) Î Merge(A.arcs(s 1 ), B.arcs(s 2 )) iff (label 1 , label 2 , x, next state 1 ) Î A.arcs(s 1 ) and (label 2 , label 3 , y, next state 2 ) Î B.arcs(s 2 ); where, using M to represent any of generalized state machines C, A or B: M.start returns, on request, the start state for machine M; M.final(state) represents probability of accepting at a given state; and M.arcs(state) returns, for a given state, transitions (a 1 , a 2 , . . . a N ) leaving the given state, where a i =(input label, output label, weight & next state).

Claim 16

The speech processing system of claim 10 further including a storage cache for storing at least a part of said on-demand expansion of said selected portion of said network cascade.

Claim 17

The speech processing system of claim 14 wherein said storage cache is implemented as a generalized state machine of the form: N.start:=M.start N.final(state):=M.final(state) N.arcs(state):=M.arcs(state) where: M represents an input machine and N represents said cache machine; M.start returns, on request, the start state for machine M; M.final(state) represents probability of accepting at a given state; and M.arcs(state) returns, for a given state, transitions (a 1 , a 1 , . . . a N ) leaving the given state, where a i =(input) label, output label, weight & next state); and corresponding functions of generalized state machine N are equivalently defined.

Claim 18

The speech processing system of claim 10 further characterized in that said selected portion of said network cascade corresponds to an unknown speech fragment of interest.

Claim 19

The speech processing system of claim 10 further including a filter means operative to remove redundant paths occurring between a pair of states in a composition created by said means for composing.

Claim 20

In a system for recognition of an unknown pattern in an input signal, said pattern being indicative of underlying information content, wherein data applied to train said system are represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, a method for input pattern evaluation by on-demand expansion of a selected portion of at least one of said network models comprising the steps of: formulating said at least one of said network models as a finite state transducer, said transducer representing said mapping function between said at least one network model and an adjacent one of said cascaded network models; selecting a state in a one of said at least one transducers having a correspondence to said selected portion of said at least one network model; composing said one of said at least one transducers with a next successively higher level of said network models to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said at least one network model; iteratively repeating said selecting step and said composing step for each successively higher level network model representing said selected portion; and evaluating an input signal pattern against a candidate pattern segment provided by one of said network models.

Claim 21

In a system for recognition of an unknown pattern in an input signal, said pattern being indicative of underlying information content, wherein data applied to train said system are represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, a method for input pattern evaluation by on-demand expansion of a selected portion of at least one of said network models comprising the steps of: formulating said at least one of said network models as a finite state transducer, said transducer representing said mapping function between said at least one network model and an adjacent one of said cascaded network models; selecting a state in a one of said at least one transducers having a correspondence to said selected portion of said at least one network model; composing said one of said at least one transducers with a next successively higher level of said network models to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said at least one network model; iteratively repeating said selecting step and said composing step for each successively higher level network model representing said selected portion; and evaluating an input signal pattern against a candidate pattern segment provided by one of said network models.

Claim 22

The method for on-demand expansion of claim 18 wherein at least one of said levels of said network cascade is represented in terms of context dependent units.

Claim 23

In a system for processing linguistic information in an input signal, wherein predetermined linguistic information established for training said system is represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, a method for evaluation of input linguistic information by on-demand expansion of a selected portion of at least one of said network models comprising the steps of: formulating said at least one of said network models as a finite state transducer, said transducer representing said mapping function between said at least one network model and an adjacent one of said cascaded network models; selecting a state in a one of said at least one transducers having a correspondence to said selected portion of said at least one network model; composing said one of said at least one transducers with a next successively higher level of said network models to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said at least one network model; iteratively repeating said selecting step and said composing step for each successively higher level network model representing said selected portion; and evaluating an input linguistic-information signal against a candidate linguistic-information segment provided by one of said network models.

Claim 24

In a system for processing linguistic information in an input signal, wherein predetermined linguistic information established for training said system is represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, a method for evaluation of input linguistic information by on-demand expansion of a selected portion of at least one of said network models comprising the steps of: formulating said at least one of said network models as a finite state transducer, said transducer representing said mapping function between said at least one network model and an adjacent one of said cascaded network models; selecting a state in a one of said at least one transducers having a correspondence to said selected portion of said at least one network model; composing said one of said at least one transducers with a next successively higher level of said network models to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said at least one network model; iteratively repeating said selecting step and said composing step for each successively higher level network model representing said selected portion; and evaluating an input linguistic-information signal against a candidate linguistic-information segment provided by one of said network models.

Claim 25

In a system for processing linguistic information, wherein predetermined linguistic information established for training said system is represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, a sub-system for providing on-demand expansion of a selected portion of at least one of said network models, comprising: decoding means for evaluating an input linguistic-information signal against a candidate linguistic-information segment provided by one of said network models means for formulating said at least one of said network models as a finite state transducer, said transducer representing said mapping function between said at least one network model and an adjacent one of said cascaded network models; means for selecting a state in a one of said at least one transducers having a correspondence to said selected portion of said at least one network model; means for composing said one of said at least one transducers with a next successively higher level of said network models to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said at least one network model; and means for iteratively applying said means for selecting and said means for composing for successively higher level network models representing said selected portion.

Claim 26

In a system for processing linguistic information, wherein predetermined linguistic information established for training said system is represented in a hierarchal cascade of network models and a linkage between successive levels of said network models is defined by a mapping function, and wherein at least one path within each said network model is defined by connections among a plurality of predetermined states, a sub-system for providing on-demand expansion of a selected portion of at least one of said network models, comprising: decoding means for evaluating an input linguistic-information signal against a candidate linguistic-information segment provided by one of said network models means for formulating said at least one of said network models as a finite state transducer, said transducer representing said mapping function between said at least one network model and an adjacent one of said cascaded network models; means for selecting a state in a one of said at least one transducers having a correspondence to said selected portion of said at least one network model; means for composing said one of said at least one transducers with a next successively higher level of said network models to prescribe a mapped portion of said next successively higher level corresponding to said selected portion of said at least one network model; and means for iteratively applying said means for selecting and said means for composing for successively higher level network models representing said selected portion.