A common problem in speech processing is the conversion of the written form of a language to a set of phonetic symbols representing the pronunciation. In this paper, we focus on an aspect of this problem specific to the Japanese language. Written Japanese consists of a mixture of three types of symbols: Kanji, Hiragana, and Katakana. We describe an algorithm for converting conventional Japanese orthography to a Hiragana-like symbol set that closely approximates the most common pronunciation of the text. The algorithm is based on two hypotheses: (1) the correct reading of a Kanji character can be determined by examining a small number of adjacent characters and (2) the number of such combinations required in a dictionary is manageable. The algorithm described here converts the input test by selecting the most probable sequence of orthographic units (n-grams) that can be concatenated to form the input text. In closed-set testing, the n-gram algorithm was shown to provide better performance than several public domain algorithms, achieving a sentence error rate of 3% on a wide range of text material. Though the focus of this paper is written Japanese, the pattern matching algorithm described here has applications to similar problems in other languages.

Texas Instruments' Voice Across Japan (VAJ) database, modeled after the highly successful Voice Across America project, consists of a wide range of diverse speech material including digit strings, yes/no questions, and phonetically-rich read sentences. The data is being collected using long distance telephone lines and an analog telephone interface. The target size is 14 items per speaker by 10,000 speakers. Greater emphasis is being placed on the collection of phonetically-rich read sentence data. Four randomly selected sentences are included in each session: one from the 512 sentence ATR PB set, and three from a 10,000 sentence set developed specifically for this project. This latter sentence set, designed to maximize the triphone coverage of the database, is described. The VAJ database is planned to be included in the Linguistic Data Consortium's (LDC) Polyphone (multi-language) database.>