Signal Transmission in the Auditory System

Contains table of contents for Section 3, an introduction and reports on six research projects.National Institutes of Health Grant R01-DC-00194National Institutes of Health Contract P01-DC-00119National Institutes of Health Fellowship F32-DC00073National Institutes of Health Grant R01-DC00238National Institutes of Health Grant R01-DC00473National Institutes of Health Grant T32-DC00006National Institutes of Health Grant T32-DC00038National Institutes of Health Contract P01-DC00361National Institutes of Health Grant R01-DC00235National Institutes of Health Contract N01-DC2240

Signal Transmission in the Auditory System

Contains table of contents for Section 3, an introduction and reports on six research projects.National Institutes of Health Grant R01-DC-00194-11National Institutes of Health Grant P01-DC00119 Sub-Project 1National Institutes of Health Grant F32-DC00073-2National Institutes of Health Contract P01-DC00119National Institutes of Health Grant R01-DC00238National Institutes of Health Gramt R01-DC00473National Institutes of Health Grant P01-DC00119National Institutes of Health Grant T32-DC00038PNational Institutes of Health Grant P01-DC00361National Institutes of Health Grant 2RO1 DC00235National Institutes of Health Contract NO1-DC2-240

Signal Transmission in the Auditory System

Contains table of contents for Section 3, an introduction and reports on six research projects.National Institutes of Health Grant RO1-DC-00194-11National Institutes of Health Grant PO1-DC00119 Sub-Project 1National Institutes of Health Grant F32-DC00073-3National Institutes of Health Contract P01-DC00119National Institutes of Health Grant R01 DC00238National Institutes of Health Grant P01-DC00119National Institutes of Health Grant T32-DC00038National Institutes of Health Contract P01-DC00361National Institutes of Health Grant R01-DC00235National Institutes of Health Contract NO1-DC2240

Automatic language identification using Gaussian mixture and hidden Markov models

Ergodic, continuous-observation, hidden Markov models (HMMs) were used to perform automatic language classification and detection of speech messages. State observation probability densities were modeled as tied Gaussian mixtures. The algorithm was evaluated on four multilanguage speech databases: a three language subset of the Spoken Language Library, a three language subset of a five language Rome Laboratory database, the 20 language CCITT database, and the ten language OGI telephone speech database. Generally, performance of a single state HMM (i.e, a static Gaussian mixture classifier comparable to the multistate HMMs, indicating that th;: quential modeling capabilities of HMMs were not exploited. 1