In this paper we describe the design and implementation of a user interface for SPICE, a web-based toolkit for rapid prototyping of speech and language processing components. We report on the challenges and experiences gathered from testing these tools in an advanced graduate hands-on course, in which we created speech recognition, speech synthesis, and smalldomain translation components for 10 different languages within only 6 weeks.

The paper describes our portable two-way speech-tospeech translation system using a completely eyesfree/hands-free user interface. This system translates between the language pair English and Iraqi Arabic as well as between English and Farsi, and was built within the framework of the DARPA TransTac program. The Farsi language support was developed within a 90-day period, testing our ability to rapidly support new languages. The paper gives an overview of the system’s components along with the individual component objective measures and a discussion of issues relevant for the overall usage of the system. We found that usability, flexibility, and robustness serve as severe constraints on system architecture and design. 1.

This paper presents a strategy for efficiently selecting informative data from large corpora of transcribed speech. We propose to choose data uniformly according to the distribution of some target speech unit (phoneme, word, character, etc). In our experiment, in contrast to the common belief that “there is no data like more data”, we found it possible to select a highly informative subset of data that produces recognition performance comparable to a system that makes use of a much larger amount of data. At the same time, our selection process is efficient and fast. Index Terms — data selection, maximum entropy, speech recognition, acoustic modeling 1.