Connectionist word-level classification in speech recognition

MS-TDNN (multistate time delay neural networks), a connectionist architecture with embedded time alignment, was proposed recently. It makes word level classification possible and efficient on speech recognition tasks. Connectionist classification at the word level (rather than the usual maximum likelihood estimation) has not been commonly used in speech recognition, and raises issues related to proper temporal modeling and global discriminant training applied at the word level. The author shows how MS-TDNNs deal with these issues in a simple and efficient way, and achieve state of the art performance on several tasks representative of different problems in speaker-independent speech recognition: telephone digits and connected spelled letters