Patent Document ID: 9972300
Application ID: 14737080

Base Claim:
1. A method for generating synthesized speech using parametric models, the method comprising the steps of: a. selecting sentences from a database of speech audio files, wherein the sentences comprise a plurality of phonemes; b. identifying a total sum of instance outliers for each of the plurality of phonemes, wherein the instance outliers comprise fundamental frequency based outliers and group delay based outliers; c. ignoring the sentences wherein the total sum of instance outliers exceeds a sentence outlier threshold and retaining sentences wherein the total sum of instance outliers meets the sentence outlier threshold; d. using the retained sentences to generate trained Hidden Markov Models; e. generating a plurality of context dependent Hidden Markov Models using the trained Hidden Markov Models, spectrum parameters, and excitation parameters, wherein the spectrum parameters and excitation parameters are extracted from the database of speech audio files using the trained Hidden Markov Models; f. analyzing a selected text and generating text excitation parameters and text spectral parameters using the plurality of context dependent Hidden Markov Models; g. generating a text excitation signal using the text excitation parameters; and h. generating a synthesized speech waveform by passing the text excitation signal and text spectral parameters into a synthesis filter.

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Claim 8:
8. The method of claim 1 wherein the step of identifying group delay based outliers further comprises: a. generating syllable alignments for each of the plurality of phonemes using a speech recognition system and a phoneme model; b. making adjustments to the syllable alignments using group delay algorithms; c. splitting the syllable alignments and analyzing the split syllable assignments for pooling information; d. generating phoneme boundaries for each of the split syllables using the phoneme model; e. determining likelihood values for each of the generated phoneme boundaries, wherein the likelihood values comprise log-likelihood values; f. determining whether generating the syllable alignment has failed or if the likelihood value is too small; and g. identifying a sum of instance outliers for each of the generated phoneme boundaries.