Tilo Sloboda

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-II-translation of spontaneous conversational speech

JANUS-II is a research system to design and test components of speech-to-speech translation systems as well as a research prototype for such a system. We focus on two aspects of the system: (1) the new features of the speech recognition component JANUS-SR, and (2) the end-to-end performance of JANUS-II, including a comparison of two machine translation strategies used for JANUS-MT (PHOENIX and GLR*).

Testing generality in JANUS: a multi-lingual speech translation system

For speech translation to be practical and useful, speech translation systems should be portable to multiple languages without substantial modification. The authors present results of expanding the English-based JANUS speech translation system to translate from spoken German sentences to English and Japanese utterances. The authors also report the results of implementing part of the linked predictive neural network (LPNN) speech recognition module on a massively parallel machine. The JANUS approach generalizes well, with overall system performance of 97%. This surpasses English-based JANUS performance.<<ETX>>

Integrating different learning approaches into a multilingual spoken language translation system

Building multilingual spoken language translation systems requires knowledge about both acoustic models and language models of each language to be translated. Our multilingual translation system JANUS-2 is able to translate English and German spoken input into either English, German, Spanish, Japanese or Korean output. Getting optimal acoustic and language models as well as developing adequate dictionaries for all these languages requires a lot of hand-tuning and is time-consuming and labor intensive. In this paper we will present learning techniques that improve acoustic models by automatically adapting codebook sizes, a learning algorithm that increases and adapts phonetic dictionaries for the recognition process and also a statistically based language model with some linguistic knowledge that increases recognition performance. To ensure a robust translation system, semantic rather than syntactic analysis is done. Concept based speech translation and a connectionist parser that learns to parse into feature structures are introduced. Furthermore, different repair mechanisms to recover from recognition errors will be described.