A parallel large vocabulary continuous speech recognition (LVCSR) algorithm for cellphone-oriented multicore processors is proposed. We introduce an acoustic look-ahead and blockwise computation to our compact LVCSR algorithm, in order to distribute its computational load to multiple CPU cores. We implement the proposed LVCSR algorithm on an evaluation board of a cellphone-oriented three CPU core chip, and show real-time processing of stand-alone LCVSR on cellphones can be achieved with recognition vocabulary of about 50,000 words. We also implement a speech-input text retrieval system using the proposed LVCSR on the same evaluation board, and confirm the ability of the proposed LVCSR algorithm to provide comfortable responses to query sentences spoken to a cellphone, without requiring any outside resources

Recent progress in large vocabulary continuous speech recognition (LVCSR) has raised the possibility of applying information retrieval techniques to the resulting text. This paper presents a novel unsupervised text segmentation method. Assuming a generative model of a text stream as a left-to-right hidden Markov model (HMM), text segmentation can be formulated as model parameter estimation and model selection using the text stream. The formulation is derived based on the variational Bayes framework, which is expected to work well with highly sparse data such as text. The effectiveness of the proposed method is demonstrated through a series of experiments, where broadcast news programs are automatically transcribed and segmented into separate news stories.

The paper describes an on-line manual page retrieval system activated by spoken queries for multimodal cellular phones. The system recognizes a user's naturally spoken queries by telephone LVCSR and searches an on-line manual with a retrieval module on a server. The user can view the retrieved data on the screen of the phone via Web access. The LVCSR module consists of a telephone acoustic model and an n-gram language model derived from a task query corpus. The adaptation method using the target manual is also presented. The retrieval module utilizes pairs of words with dependency relations and also distinguishes affirmative and negative expressions to improve precision. The proposed system gives 82.6% keyword recognition accuracy and 77.5% task achievement rate. The field trial of the system is now underway.

We present an automatic speech-to-speech translation system for personal digital assistants (PDAs) that helps oral communication between Japanese and English speakers in various situations while traveling. Our original compact large vocabulary continuous speech recognition engine, compact translation engine based on a lexicalized grammar, and compact Japanese speech synthesis engine lead to the development of a Japanese/English bi-directional speech translation system that works with limited computational resources.

Some groupware products support office jobs by providing cooperative functions such as workflow management.However, they cannot support documentation jobs because the jobs need individual creativity which is difficult to share.This paper proposes a 'Know-how Sharing Agent' which allows individual creativity used for documentation jobs to be shared.The Know-how Sharing Agent supports document creation by preparing the most exemplary document and its documentation operations.The documentation operations were also saved by the agent automatically when the document was created.This type of agent thus promotes documentation activities by sharing the know-how needed for documentation.