Accuracy of text-to-speech synthesis

1. A method comprising:

detecting, by at least one processor, occurrence of an out-of-vocabulary word in a text sample;

detecting a likelihood that the out-of-vocabulary word will be mispronounced using a primary text-to-speech synthesizer associated with a primary language;

receiving feedback from a source other than the primary text-to-speech synthesizer, the feedback indicating a conversion in accordance with a secondary language of the out-of-vocabulary word into a corresponding audio output;

storing the feedback in a repository:

generating, based on the feedback and by a secondary text-to-speech synthesizer associated with the secondary language, a first audio pronunciation of the out-of-vocabulary word pronounced in accordance with a native secondary language speaking person speaking the secondary language; and

generating, in accordance with a native primary language speaking person speaking the primary language, a second audio pronunciation of the out of vocabulary word.

2. The method as in claim 1 , wherein the occurrence is a first occurrence of the out-of-vocabulary word, the method further comprising:

detecting a second occurrence of the out-of-vocabulary in a subsequent text sample;

accessing the feedback in the repository; and

determining, based on a setting associated with the second text-to-speech synthesizer, whether to provide the first audio pronunciation of the out-of-vocabulary word or the second audio pronunciation of the out-of-vocabulary word.

3. The method as in claim 1 , wherein the primary text-to-speech synthesizer converts the text sample in accordance with the primary language; and

wherein the feedback indicates conversion of the out-of-vocabulary word into a corresponding audio output in accordance with a foreign language with respect to the primary language.

4. The method as in claim 1 , wherein receiving the feedback includes:

receiving the feedback from a human reviewer that provides the conversion of the out-of-vocabulary word into the corresponding audio output.

5. The method as in claim 1 , further comprising:

initiating distribution of the feedback in the repository over a network to each of multiple remotely located text-to-speech synthesizer systems, each of the remotely located text-to-speech synthesizers configured to convert respective text samples for respective clients that access the remotely located text-to-speech synthesizers.

6. The method as in claim 1 , wherein detecting the likelihood that the out-of-vocabulary word will be mispronounced using the primary text-to-speech synthesizer includes:

implementing the primary text-to-speech synthesizer in a first language, the out-of-vocabulary word being absent from a lexicon lookup of the first language.

7. The method as in claim 6 , wherein receiving the feedback includes:

analyzing the out-of-vocabulary word via a secondary text-to-speech synthesizer that attempts to convert the out-of-vocabulary in a foreign language with respect to the first language; and

producing the feedback in response to detecting that the out-of-vocabulary word is present in a lexicon lookup used by the secondary text-to-speech synthesizer to convert text into speech.

8. A method comprising:

implementing, by at least one processor, a lexicon lookup algorithm via first text-to-speech hardware to produce a first audio output for each word in a set of multiple words comprising one or more words from a base language and one or more words from a foreign language;

implementing a grapheme-to-phoneme algorithm comprising one or more grapheme-to-phoneme rules via second text-to-speech hardware to produce a second audio output for each word in the set of multiple words;

comparing the first audio output and the second audio output by analyzing instances in which the lexicon lookup algorithm produces a different audio output than the grapheme-to-phoneme algorithm for respective text; and

generating a set of predictors based on the comparing, the set of predictors indicating circumstances in which use of the one or more grapheme-to-phoneme rules results in identifying one or more audio output representations that correspond to one or more words from the foreign language.

9. The method as in claim 8 , further comprising:

classifying each of the multiple words by:

generating a first class of words to include each respective word of the multiple words in which the lexicon lookup algorithm and the grapheme-to-phoneme algorithm produce a substantially different audio output representation; and

generating a second class of words to include each respective word of the multiple words in which the lexicon lookup algorithm and the grapheme-to-phoneme algorithm produce a substantially same audio output representation; and generating the set of predictors based on the classifying.

10. The method as in claim 8 , further comprising:

for each of the multiple words:

selecting a word from the multiple words;

utilizing the first text-to-speech hardware to generate a first audio output representative of the selected word;

utilizing the second text-to-speech hardware to generate a second audio output representative of the selected word;

comparing the first audio output to the second audio output representation; and

classifying the respective first audio output and the second audio output as being either substantially the same or substantially different.

11. The method as in claim 8 , wherein the set of predictors indicating indicate circumstances in which use of the one or more grapheme-to-phoneme rules results in generation of substantially different audio output representations by the lexicon lookup algorithm and by the grapheme-to-phoneme algorithm.

12. The method as in claim 11 , further comprising:

utilizing the set of predictors to train a classification model.

13. The method as in claim 12 , further comprising:

receiving a text sample on which to perform text-to-speech synthesis; and

utilizing the classification model to detect which out-of-vocabulary words in the text sample are likely to be mispronounced during the text-to-speech synthesis of the text sample.

14. The method as in claim 9 , further comprising:

identifying which subset of the multiple words the lexicon lookup algorithm produces a different audio output than the grapheme-to-phoneme algorithm;

analyzing the subset of words to identify instances in which the grapheme-to-phoneme algorithm produces an improper audio output for words in the subset;

producing a set of rules based on the instances; and

utilizing the set of rules to train a classification model, the classification model configured to detect which out-of-vocabulary words in a future received text sample are likely to be mispronounced during text-to-speech synthesis of the text sample.

15. The method as in claim 14 , further comprising:

receiving a text sample on which to perform text-to-speech synthesis; and

utilizing the classification model to detect which out-of-vocabulary words in the text sample are likely to be mispronounced during the text-to-speech synthesis of the text sample.

16. A method comprising:

detecting, by at least one processor, occurrence of an out-of-vocabulary word in a text sample to be converted into audio output by detecting that the out-of-vocabulary word is not located in a lexicon associated with a default language;

determining a probability that the out-of-vocabulary word will be mispronounced using a text-to-speech synthesizer;

in response to the probability that the out-of-vocabulary word will be mispronounced being below a first threshold probability, producing, via a first text-to-speech synthesizer configured to generate audio in accordance with the default language, a first audio output of the entire out-of-vocabulary word and any words in the text sample that are located in the lexicon associated with the default language; and

in response to the probability that the out-of-vocabulary word will be mispronounced meeting a second threshold probability, producing, via a second text-to-speech synthesizer configured to generate audio in accordance with a foreign language, a second audio output of the out-of-vocabulary word.

17. The method as in claim 16 further comprising:

utilizing the first text-to-speech synthesizer to produce an audio output of at least one word other than the out-of-vocabulary word in the text sample;

utilizing the second text-to-speech synthesizer to produce the second audio output of the out-of-vocabulary word; and

combining the audio output of the at least one word and the second audio output of the out-of-vocabulary word to produce an audio output.

18. The method as in claim 16 , wherein the second audio output of the out-of-vocabulary word comprises an audio pronunciation of the out-of-vocabulary word pronounced in accordance with a native default language speaking person speaking the default language.

19. The method as in claim 16 , wherein detecting occurrence of the out-of-vocabulary word in the text sample includes:

performing a morpho-syntactic analysis to one or more words in the text sample to detect the out-of-vocabulary word.

20. The method as in claim 16 , wherein the second audio output of the out-of-vocabulary word comprises an audio pronunciation of the entire out-of-vocabulary word pronounced in accordance with a native foreign language speaking person speaking the foreign language.