Previously, we have developed Speech-Aware Multimedia (SAM) which controls a WWW browser using English speech. We have extended its capability to use Japanese speech to browse Japanese pages, and developed a prototype using speaker-independent, continuous speech recognition with Japanese context-dependent phonetic models. Some challenges not seen in English include: segregation of Japanese text into word units for optional silence insertion, Japanese text to phone conversion and accommodation of English link names embedded in Japanese pages. In order to accomplish the first two, we modified a public-domain dictionary look-up tool for segmentation and to accommodate the heuristics required for improved text-to-phone conversion accuracy. Preliminary tests show that the conversion result contains the correct phone sequence over 97% of the time, and the prototype correctly understands the input speech 91.5% of the time.

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No abstract available.

Vocabulary-independence of speech recognition systems has become an important issue because of the need for flexible vocabulary and the high cost of speech corpus collection. We outline the necessary steps to achieve the goal of vocabulary-independent speech recognition, and relate our experimental experience with telephone speech recognition. Two sets of experiments were conducted: (1) 34-command recognition, in which we compared vocabulary-independent (VI) and vocabulary-dependent (VD) systems as well as phonetic and word based systems, and (2) 42-city name recognition, in which our vocabulary independent recognition performance (8.5% W. Err.) was much better than the VI performance (18%) reported by the Oregon Graduate Institute (OGI) and very close to OGI's VD performance (8%). We conclude that we have made some strides toward vocabulary independence, but much remains to be done; we identify the areas of improvement that are likely to lead to the goal.>

A method for automatic time alignment of orthographically transcribed speech using supervised speaker-independent automatic speech recognition based on the orthographic transcription, an online dictionary, and HMM phone models is presented. This method successfully aligns transcriptions with speech in unconstrained 5 to 10 min conversations collected over long-distance telephone lines. It requires minimal manual processing and generally produces correct alignments despite the challenging nature of the data. The robustness and efficiency of the method make it a practical tool for very large speech corpora.>

The authors describe a stochastic unification grammar system that is a generalization of the conventional hidden Markov model (HMM) approach. Unification grammars concisely model context, providing a more powerful characterization of the acoustic data than the first-order Markov process. It is shown that this approach generalizes traditional FSA (finite-state automaton)-based HMM systems and that a stochastic chart parsing algorithm produces the exact same solutions as an existing FSA-based system. The shift from automata to grammars allows efficient processing of complex language models by hypothesizing symbols once per frame, no matter how many times they are needed. As an added benefit, the chart parsing algorithm allows parallel processing of lower level hypotheses autonomously with no fundamental algorithm changes.>