Abstract
This chapter discusses the structure of acoustic models and training algorithms for speech recognition. As is generally recognized, high acoustic model complexity demands more training data. One effective solution is tying at multiple levels such as allophone, state, distribution, or parameter levels. Tied structures such as generalized triphones, state tying, and tied mixtures, have been one of the main streams of research in acoustic modeling of speech. They offer not only precise and robust modeling, but also significant computational advantage. This chapter introduces the Hidden Markov Network (HMnet) which is derived by the Successive State Splitting algorithm. The ultimate goal is an acoustic model with a fully tied acoustic structure in four levels. Vector Field Smoothing (VFS) for speaker adaptation is also discussed for more efficient training of acoustic models.
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© 1996 Kluwer Academic Publishers
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Sagayama, S. (1996). Hidden Markov Network for Precise and Robust Acoustic Modeling. In: Lee, CH., Soong, F.K., Paliwal, K.K. (eds) Automatic Speech and Speaker Recognition. The Kluwer International Series in Engineering and Computer Science, vol 355. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1367-0_7
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DOI: https://doi.org/10.1007/978-1-4613-1367-0_7
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