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Acoustic / Lexical Model

Explore the interplay of acoustic and lexical models in speech recognition to optimize signal processing, word mapping, and error reduction for enhanced accuracy. Experiment with dynamic adaptability to improve real-world application performance.

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Acoustic / Lexical Model

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  1. Acoustic / Lexical Model Derk Geene

  2. Speech recognition • P(words|signal)= P(signal|words) P(words) / P(signal) • P(signal|words): Acoustic model • P(words): Language model • Idea: Maximize P(signal|words) P(words) • Today: Acoustic model

  3. Variability • Variation • Speaker • Pronunciation • Environmental • Context • Static acoustic model will not work in real applications. • Dynamically adapt P(signal|words) while using the system.

  4. Measuring errors (1) • 500 sentences of 6 – 10 words each from 5 to 10 different speakers. • 10% relative error reduction • Training set / Development set • First decide optimal parameter settings.

  5. Word recognition errors: Substitution Deletion Insertion Correct: Did mob mission area of the Copeland ever go to m4 in nineteen eighty one? Recognized: Did mob mission area ** the copyland ever go to m4 in nineteen east one? Measuring errors (2)

  6. Correct: The effect is clear Recognised: Effect is not clear Error Rate One by one: 75% Word error rate Subs + Dels + Ins Word error rate=100% x #words in correct sentence Measuring errors (3)

  7. Units of speech (1) • Modeling is language dependent.fixme • Modeling unit • Accurate • Trainable • Generalizable

  8. Units of speech (2) • Whole-word models • Only suitable for small vocabulary recognition • Phone models • Suitable for large vocabulary recognition • Problem: over-generalize  less accurate • Syllable models

  9. Context dependency (1) • Recognition accuricy can be improved by using context-dependent parameters. • Important in fast / spontanious speech. • Example: the phoneme /ee/

  10. Peat • Wheel

  11. Triphone model: phonetic model that takes into consideration both the left and the right neightbouring phones. If two phones have the same identity, but different left or right contexts, there are considered different triphones. Interword context-dependent phones. Place in the word: Beginning Middle End Context dependency (2)

  12. Context dependency (3) • Stress • Longer duration • Higher pitch • More intensity • Word-level stress • Import – Import • Italy – Italian • Sentence-level stress • I did have dinner. • I did have dinner.

  13. Radio • Radio

  14. Context dependency (4) • Vary much triphones. 503 = 125.000 • Many phonemes have the same effects /b/ & /p/ labial (pronounces by using lips) /r/ & /w/ liquids • Clustered acoustic-phonetic units Is the left-context phone a fricative? Is the right-context phone a front vowel?

  15. Acoustic model • After feature extraction, we have a sequence of feature vectors, such as the MFCC vector, as input data. Feature stream Segmentation and labeling Phonemes / units Lexical access problem Words

  16. Acoustic model • Signal  Phonemes • Problem: phonemes can be pronounced differently • Speaker differences • Speaker rate • Microphone

  17. Acoustic model • Phonemes  Words • The three major ways to do this: • Vector Quantization • Hidden Markov Models • Neural Networks

  18. Acoustic model • Problem: Multiple pronunciations: • Dialect variation • Coarticulation 0,5 m 0,2 0,5

  19. The End

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