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Text-To-Speech Synthesis

Text-To-Speech Synthesis. An Overview. What is a TTS System. Goal A system that can read any text Automatic production of new sentences Not just audio playback Simple voice response systems Definition

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Text-To-Speech Synthesis

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  1. Text-To-Speech Synthesis An Overview

  2. What is a TTS System • Goal • A system that can read any text • Automatic production of new sentences • Not just audio playback • Simple voice response systems • Definition • The production of speech by machines, by way of the automatic phonetization of the sentences to utter

  3. Text-To-Speech • Text Processing • Text Normalization • Pronunciation • Timing and Intonation • Speech Generation • Segmental Concatenation • Waveform Synthesis

  4. Functional Diagram TTS Synthesizer Natural Language Processing Digital Signal Processing Narrow Phonetic Transcription Morphosyntactic Analysis Letter-to-Sound Prosody Generation Mathematical Models Algorithms Computations Phones Prosody Text Speech

  5. The Natural Language Processing Module Text NLP Module Preprocessor Morphological Analyzer Contextual Analyzer Syntactic and Prosodic Parser Morphosyntactic Analyzer Letter-to-Sound Module Natural Prosody Generator Phone Names Prosody

  6. Text PreprocessingChallenges • Text Segmentation – Tokenization • (i) () (know) ( ) (1) (,) (000) ( ) (words) • Sentence End Detection • Jones lives at the end of St. James St. • Normalization • Abbreviations • κ.: κύριος, κυρίου, κύριο • κ.: κύριος, κιλό • Acronyms • ΦΠΑ, ΔΕΗ, ΝΑΤΟ • Numbers • 1.023,32 12/1/2002 13:23 12.15πμ

  7. Text PreprocessingDealing with Non-Standard Words • Tokenizer • Breaks up single tokens that need splitting • 12:35AM -> 12 : 35 AM • Classifier • Determines the most likely class for a given token • January 1956 – 1956 potatoes • Expansion Module • Methods for expanding numbers and classes that can be handled algorithmically

  8. Text PreprocessingDealing with Non-Standard Words • Not all tokens can be handled with a deterministic set of rules • Methods for designing domain-dependent expansion and tagging modules • Supervised: work on tagged text corpus • Unsupervised: work on raw text • Determines the probability of a tag t given the observed string o p(o): the probability of the observed text p(t): the prior probability of observing the tag t in the text p(o|t): a trigram letter language model for predicting observations of a particulat tag t

  9. Morphological Analysis • Function Words • Determiners, Pronouns, Prepositions, Conjunctions • Skeleton of sentence • Stored in lexicon, along with pronunciation • Content Words • Inflection + Compounding • Used for pronunciation and stressing

  10. Synthesis • Input • Sequence of phonemes • Prosodic Information • Output • Digital Speech

  11. Synthesis Strategies • Synthesis by Rule • Cognitive approach of the phonation mechanism • Speech is produced by mathematical rules that formally describe the influence of phonemes on one another • Synthesis by Concatenation • Limited knowledge of the data to be handled • Elementary speech units are stored in a database and then concatenated and processed to produce the speech signal

  12. Speech Corpus Parametric Speech Corpus Rule Database Speech Analysis Rule Finding Synthesis by RuleFunctional Diagram Phone Names Prosody DSP Module Speech Science Rule Matching Signal Processing Signal Synthesis Speech

  13. Synthesis by RuleAnalysis and Synthesis • Preparation • Words are read by professional speaker • Data Parameterization through speech analyzer • Rule extraction (manual) • Trial and Error Optimization • Synthesis • Rules are matched to phonetic input • Production of parametric signal • Synthesis of speech signal by re-implementing analysis model

  14. Synthesis by RuleSegmental Quality • Rule Efficiency • Corpus Quality • Choice of utterances and recording quality • Intrinsic Errors: Accuracy of model describing high-quality speech • Even simple analysis-resynthesis may produce problems! • Extrinsic Errors: Parameter extraction algorithm • Improvements during Trial-Error tuning

  15. Synthesis by RuleFormant Synthesizers • Speech is a dynamic evolution of up to 60 parameters • Formant, antiformant frequencies and bandwidths • Glottal waveforms • Almost free of modeling errors • Difficult to estimate • Time consuming • Intensive trial-error testing to cope with extrinsic errors • Signal Buzziness – Low Signal Quality • High-quality synthesis rules are yet to be discovered

  16. Speech Corpus Selective Segmentation Speech Segment DB Speech Analysis Parametric Segment DB Equalization Speech Coding Synthesis by ConcatenationFunctional Diagram Phone Names Prosody DSP Module Speech Science Segment Info Segment List Generation Signal Processing Prosody Matching Synthesis Segment DB Speech Decoding Concatenation Signal Synthesis Speech

  17. Synthesis by ConcatenationAnalysis – Database Preparation • Choose the appropriate speech units • Diphones, Half-Syllables and Triphones • Compile and record utterances • Segment signal and extract speech units • Store segment waveforms (along with context) and extended information in database • Extract parameters and create parametric segment database • Useful for data compaction • Easier prosody matching and modification • Perform amplitude equalization to prevent mismatches

  18. Synthesis by ConcatenationUnit Database Issues • Very large combinatorial space of combinations of phonemes and prosodic contexts • In English: 43 phones, 79,507 possible triphones, only 70,000 used • Which of them should we keep? • Unit Selection vs Concatenative Synthesis • We record a large speech corpus • In unit selection, the corpus is segmented into phonetic units, indexed, and used as-is • Unit selection is made on-line • In Concatenative synthesis, the selection is made off-line and manually!

  19. Concatenating SegmentsThe PSOLA Method • Pitch Synchronous Overlap and Add • A window (2-pitch periods long) is multiplied with the signal • The signal is broken into a set of localized signals (non-zero only at the window intervals) • Pitch Modification • Relative shifting of localized signals • Spacing reflects pitch duration • Good result for modification factor β=[0.6 – 1.5] • Duration • Localized signals are added or deleted from output

  20. Concatenative and Rule Based SynthesisComparison • Concatenative Synthesis is the state-of-the-art • Storage is of little concern now • Storing the segment database is no longer an issue • Advances in ensuring smoothness in concatenations • Rule-based synthesis output used to be smoother • Certain sounds are too hard to be produced by rule • Vowels are easy to create by rule • Bursts, voiceless stops are too difficult, we do not fully understand their production mechanisms

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