Thomas Polzin

Recognizing emotion in speech

The paper explores several statistical pattern recognition techniques to classify utterances according to their emotional content. The authors have recorded a corpus containing emotional speech with over a 1000 utterances from different speakers. They present a new method of extracting prosodic features from speech, based on a smoothing spline approximation of the pitch contour. To make maximal use of the limited amount of training data available, they introduce a novel pattern recognition technique: majority voting of subspace specialists. Using this technique, they obtain classification performance that is close to human performance on the task.

Recent advances in Janus: a speech translation system

We present recent advances from our efforts in increasing coverage, robustness, generality and speed of JANUS, CMUs speech-to-speech translation system. JANUS is a speaker-independent system translating spoken utterances in English and also in German into one of German, English or Japanese. The system has been designed around the task of conference registration (CR). It has initially been built based on a speech database of 12 read dialogs, encompassing a vocabulary of around 500 words. We have since been expanding the system along several dimensions to improve speed, robustness and coverage and to move toward spontaneous input.

JANUS 93: towards spontaneous speech translation

We present first results from our efforts toward translation of spontaneously spoken speech. Improvements include increasing coverage, robustness, generality and speed of JANUS, the speech-to-speech translation system of Carnegie Mellon and Karlsruhe University. The recognition and machine translation engine have been upgraded to deal with requirements introduced by spontaneous human to human dialogs. To allow for development and evaluation of our system on adequate data, a large database with spontaneous scheduling dialogs is being gathered for English, German and Spanish.<<ETX>>

Learning complex output representations in connectionist parsing of spoken language

Due to robustness, learnability and ease of integration of different information sources, connectionist parsing systems have proven to be applicable for parsing spoken language, However, most proposed connectionist parsers do not compute and represent complex structures. These parsers assign only a very limited structure to a given input string. For spoken language translation and data base access, more detailed syntactic and semantic representation is needed. In the present paper, the authors show that arbitrary linguistic features and arbitrary complex tree structures can indeed also be learned by a connectionist parsing system.<<ETX>>