Closed rhdunn closed 11 years ago
Deferring this as it is too broad and needs implementation experience to better define the scope. That is, it is better to have smart and accurate content detection. This is especially true when the content is mixed (e.g. a chess match performed over email).
Most text-to-speech engines are inflexible in the way that content handling (ordinals, cardinals, percentages, dates, etc.) are handled, along with acronyms.
For a given language, the following things need to be provided:
[*] Strictly speaking, an exception dictionary should be created with any word from the pronunciation dictionary that cannot be constructed using the letter to phoneme and phoneme to phoneme rules. This allows the exception dictionary to be small and the letter to phoneme rules to be tested and verified against a reference set of words.
It should be possible to choose the classification scheme and abbreviation rules for the document being read. For example, using email/SMS abbreviations in email documents.
Where possible, the text-to-speech engine should select appropriate defaults, but this behaviour should be overridable (e.g. supressing US state abbreviation expansion on addresses).
For the UI, this could be handled as a drop-down with a list of profiles ("email/sms", "novel", "technical", "chess", etc.)