One of the issues I face in developing the Alexa skills for LMS is that I have to search your library -textually- based on the speech-to-text of what Alexa thinks she -heard-. This can be problematic with similar-sounding words like pair/pear, hear/here, knot/not, ate/eight, flour/flower, etc. I can only fuzzy match / post-process the returned values if the searched term is among the search results. Of course a search for a song with -flower- in the title will not return a song with -flour- - end of story. Asking Alexa again will not help because she will hear the same thing every time and return the same search-for text.
A potential workaround for this would be to have a plugin that extends the scanning process to add columns to the database that contain computed *Double Metaphone* (DM) values for tags. There is a CPAN lib for DM and all it does is produce a text mapping from a word to something that sounds like the word according to pre-defined rules. Some examples: Flour, flower -> FLR Not, Knot -> NT Hear, Here -> HR Ate, Eight -> AT So the song title 'True Colors' would become TRKL, but so would Tru Colors, True Colours and even Trew Cullers. This means that I can match based on *sound* of the title rather than spelling and therefore more easily find stuff in your library. With the added benefit that this would work in the GUI interface too and help you find stuff with misspelled tags. Just curious if anybody ever experimented with this kind of thing before? Would the increased DB size and time-to-rescan be an issue at all? ------------------------------------------------------------------------ philchillbill's Profile: http://forums.slimdevices.com/member.php?userid=68920 View this thread: http://forums.slimdevices.com/showthread.php?t=112531 _______________________________________________ discuss mailing list discuss@lists.slimdevices.com http://lists.slimdevices.com/mailman/listinfo/discuss
