Details

Autor(en) / Beteiligte

• Isotani, R.
• Matsunaga, S.

Titel

A stochastic language model for speech recognition integrating local and global constraints

Ist Teil von

• Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, 1994, Vol.ii, p.II/5-II/8 vol.2

Ort / Verlag

IEEE

Erscheinungsjahr

1994

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• This paper describes a speech recognition system that uses a new stochastic language model that integrates local and global constraints. Dependencies within adjacent words are used as local constraints in the same way as in conventional word N-gram models. To capture the global constraints between non-contiguous words, the sequence of the function words and that of the content words are taken into account. Furthermore, it is shown that, assuming an independence between local- and global constraints, the number of parameters to be estimated and stored is greatly reduced. The proposed language model is incorporated into a speech recognizer based on the time-synchronous Viterbi algorithm, and compared with the word bigram model and trigram model. The experimental results show that the proposed method is able to capture linguistic constraints effectively.< >