Vom Sprachsignal zum Wort/From the Speech Signal to the Word
Mapping the continuous and highly variable speech signal to discrete words
in the memory is not a trivial problem automatic and human speech
perception. The course will cover methods used in today's automatic speech
recognition systems, like Hidden-Markov models (HMMs) and artificial neural
nets (ANNs), which rely on stochastic models and which can operate without
phonetic and linguistic knowledge. Next to these, the course will
introduce alternative knowledge based approaches that are partly oriented
towards human processes in speech perception and speech production.
The relevant material is introduced without an expectation of prior
mathematical or technical knowledge and the issues will be covered
in non-mathematical language.
- Becchetti, C. and L. P. Ricotti (1999)
Speech Recognition - Theory and C++ Implementation. Chichester:
John Wiley & Sons.
- De Mori, R., (Ed.) (1998)
Dialogues with Computers. London: Academic Press.
- Jelinek, F. (1997)
- Statistical Methods
for Speech Recognition. Cambridge: MIT Press.
- O'Shaughnessy, D. (2000)
- The Handbook of
Phonetic Sciences. Piscataway: IEEE Press.
Knowledge based approaches:
- Ainsworth, W. A. (1997)
- "Some approaches to
automatic speech recognition." In W. J. Hardcastle and J. Laver The
Handbook of Phonetic Sciences. Oxford: Blackwell: 721-743.
- Fohr, D., J.-P. Haton, and Y. Laprie (1994)
"Knowledge-based techniques in acoustic-phonetic decoding of speech:
interest and limitations," International Journal of Pattern Recognition
and Artificial Intelligence 8: 133-153.
- Glass, J. R. and V. W. Zue (1994)
recognition, automatic: knowledge based methods." In R. E. Asher and J. M.
Y. Simpson The Encyclopedia of Language and Linguistics. Oxford:
Pergamon Press, Vol. 8: 4231-4241.