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Adaptation Techniques in Automatic Speech Recognition

Adaptation Techniques in Automatic Speech Recognition. Tor Andr é Myrvoll Telektronikk 99(2), Issue on Spoken Language Technology in Telecommunications, 2003. Goal and Objective. Make ASR robust to speaker and environmental variability.

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Adaptation Techniques in Automatic Speech Recognition

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  1. Adaptation Techniques in Automatic Speech Recognition Tor André Myrvoll Telektronikk 99(2), Issue on Spoken Language Technology in Telecommunications, 2003.

  2. Goal and Objective • Make ASR robust to speaker and environmental variability. • Model adaptation: Automatically adapt a HMM using limited but representative new data to improve performance. • Train ASRs for applications w/ insufficient data.

  3. What Do We Have/Adapt? • A HMM based ASR trained in the usual manner. • The output probability is parameterized by GMMs. • No improvement when adapting state transition probabilities and mixture weights. • Difficult to estimate  robustly. • Mixture means can be adapted “optimally” and proven useful.

  4. Adaptation Principles • Main Assumption: Original model is “good enough”, model adaptation can’t be re-training!

  5. Offline Vs. Online • If possible offline (performance uncompromised by computational reasons). • Decode the adaptation speech data based on current model. • Use this to estimate the “speaker-dependent” model’s statistics.

  6. Online Adaptation Using Prior Evolution. • Present posterior is the next prior.

  7. MAP Adaptation • HMMs have no sufficient statistics => can’t use conjugate prior-posterior pairs. Find posterior via EM. • Find prior empirically (multi-modal, first model estimated using ML training).

  8. EMAP • All phonemes in every context don’t occur in adaptation data; Need to store correlations between variables. • EMAP only considers correlation between mean vectors under jointly Gaussian assumption. • For large model sizes, share means across models.

  9. Transformation Based Model Adaptation • ML • MAP • Estimate a transform T parameterized by .

  10. Bias, Affine and Nonlinear Transformations • ML estimation of bias. • Affine transformation. • Nonlinear transformation ( may be a neural network).

  11. MLLR • Apply separate transformations to different parts of the model (HEAdapt in HTK).

  12. SMAP • Model the mismatch between the SI model (x) and the test environment. • No mismatch • Mismatch •  and  estimated by usual ML methods on adaptation data.

  13. Adaptive Training • Gender dependent model selection • VTLN (in HTK using WARPFREQ)

  14. Speaker Adaptive Training • Assumption: There exists a compact model (c),which relates to all speaker-dependent model via an affine transformation T (~MLLR). The model and the transformation are found using EM.

  15. Cluster Adaptive Training • Group speakers in training set into clusters. Now find the cluster closest to the test speaker. • Use Canonical Models

  16. Eigenvoices • Similar to Cluster Adaptive Training. • Concatenate means from ‘R’ speaker dependent model. Perform PCA on the resulting vector. Store K << R eigenvoice vectors. • Form a vector of means from the SI model too. • Given a new speaker, the mean is a linear combination of SI vector and eigenvoice vector.

  17. Summary • 2 major approaches: MAP (&EMAP) and MLLR. • MAP needs more data (use of a simple prior) than MLLR. MAP --> SD model. • Adaptive training is gaining popularity. • For mobile applications, complexity and memory are major concerns.

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