11. The method of claim 8, wherein said second comparative topic metric is ##EQU19## where T.sub.j is the j-th topic in said battery, where k is the number of words in said word string, where said current word is w.sub.m+1, where said prior word is w.sub.m-k+2, where P([ . . . ].vertline.T.sub.i) is said likelihood measure for a string of words [ . . . ] in a context of topic T.sub.i, where max.sub.j.noteq.i P([ . . . ].vertline.T.sub.j) is a maximum value of said likelihood measure over all topics T.sub.j in said battery except topic T.sub.i, and where R.sub.i is computed for all values l=m+1, m, . . . , m-k+2.
11. The method of claim 8, wherein said second comparative topic metric is ##EQU19## where T.sub.j is the j-th topic in said battery, where k is the number of words in said word string, where said current word is w.sub.m+1, where said prior word is w.sub.m-k+2, where P([ . . . ].vertline.T.sub.i) is said likelihood measure for a string of words [ . . . ] in a context of topic T.sub.i, where max.sub.j.noteq.i P([ . . . ].vertline.T.sub.j) is a maximum value of said likelihood measure over all topics T.sub.j in said battery except topic T.sub.i, and where R.sub.i is computed for all values l=m+1, m, . . . , m-k+2. 12. The method of claim 11 wherein said likelihood measure is computed using t-gram language models.