Rapid Language Adaptation Tools and Technologies for Multilingual Speech Processing Systems
Tanja Schultz, University of Karlsruhe and Carnegie Mellon University
The performance of speech and language processing technologies has improved dramatically over the past decade, with an increasing number of systems being deployed in a large variety of applications, such as spoken dialog systems, speech summarization and information retrieval systems, and speech translation systems. Most efforts to date were focused on a very small number of languages with large number of speakers, economic potential, and information technology needs of the population. However, speech technology has a lot to contribute even to those languages that do not fall into this category. Languages with a small number of speakers and few linguistic resources may suddenly become of interest for humanitarian and military reasons. Furthermore, a large number of languages are in danger of becoming extinct, and ongoing projects for preserving them could benefit from speech technology.
With more than 6900 languages in the world and the need to support multiple input and output languages, the most important challenge today is to port speech processing systems to new languages rapidly and at reasonable costs. Major bottlenecks are the lack of data and language conventions, and the gap between technology and language expertise. The lack of data results from the fact that today’s speech technologies heavily rely on statistically based modeling schemes, such as Hidden Markov Models and n-gram language modeling. Although statistical modeling algorithms are mostly language independent and proved to work well for a variety of languages, the parameter estimation requires vast amounts of training data. Large-scale data resources are currently available for less than 100 languages and the costs for these collections are prohibitive to all but the most widely spoken and economically viable languages. In addition, a surprisingly large number of languages or dialects are used for spoken communication but lack standardized writing systems which hinders web harvesting of large text corpora or the construction of dictionaries and lexicons. Last but not least, despite the well-defined process of system building it is very cost- and time consuming to handle language-specific peculiarities, and it requires substantial language expertise. Unfortunately, it is extremely difficult to find system developers who simultaneously have the necessary technical background and significant insight into the language in question. Consequently, one of the central issues in developing systems in many input and output languages is the challenge of bridging the gap between language and technology expertise.
In my talk I will introduce state-of-the-art techniques for rapid language adaptation and present existing solutions to overcome the ever-existing problem of data sparseness and the gap between language and technology expertise. I will describe the building process for speech recognition and speech synthesis components for new unsupported languages and introduce tools to do this rapidly and at lost costs. The talk describes the SPICE Toolkit (Speech Processing - Interactive Creation and Evaluation), a web based toolkit for rapid language adaptation to new languages. The methods and tools implemented in SPICE enables user to develop speech processing components, to collect appropriate data for building these models, and to evaluate the results allowing for iterative improvements. Building on existing projects like GlobalPhone and FestVox, knowledge and data are shared between recognition and synthesis; this includes phone sets, pronunciation dictionaries, acoustic models, and text resources. SPICE is an online service (http://cmuspice.org). By archiving the data gathered on-the-fly from many cooperative users we hope to significantly increases the repository of languages and resources and make the data and components for new languages available at large to the community. By keeping the users in the developmental loop, SPICE tools can learn from their expertise to constantly adapt and improve. This will hopefully revolutionize the system development process for new languages.
Tanja Schultz is a Full Professor at the Computer Science Department of Karlsruhe University in Germany and an Assistant Research Professor at the Language Technologies Institute at Carnegie Mellon University. She is the director of the Cognitive Systems Lab, director of the Center for Visually Impaired Students, and the Associate Director of InterACT.
Her research activities focus on human-human communication and human-machine interfaces with a particular area of expertise in rapid language adaptation of speech recognition and translation systems. She is the developer of GlobalPhone, a multilingual text and speech database in 20 languages, gives key-notes and invited talks on the topic of multilingual speech processing, and recently co-edited a book on this subject together with Katrin Kirchhoff. At ICASSP 2008 she presented a tutorial on multilingual speech processing together with Alan W Black.
In 2001 Tanja Schultz was awarded with the FZI price for her outstanding Ph.D. thesis on language independent and language adaptive speech recognition. In 2002 she received the Allen Newell Medal for Research Excellence from Carnegie Mellon for her contribution to Speech-to-Speech Translation and the ISCA best paper award for her publication on language independent acoustic modeling. In 2005 she was awarded the Carnegie Mellon Language Technologies Institute Junior Faculty Chair. Tanja Schultz is the author of more than 150 articles published in books, journals, and proceedings. She is a member of the IEEE Computer Society, the International Speech Communication Association ISCA, the European Language Resource Association, the Society of Computer Science (GI) in Germany, and currently serves as elected ISCA Board member, on several program committees, and review panels.