Phoneme-to-Text Transcription System with an Infinite Vocabulary

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 729–736, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Phoneme-to-Text Transcription System with an Infinite Vocabulary Shinsuke Mori Daisuke Takuma Gakuto Kurata IBM Research, Tokyo Research Laboratory, IBM Japan, Ltd. 1623-14 Shimotsuruma Yamato-shi, 242-8502, Japan mori@fw.ipsj.or.jp Abstract The noisy channel model approach is suc- cessfully applied to various natural lan- guage processing tasks. Currently the main research focus of this approach is adaptation methods, how to capture char- acteristics of words and expressions in a target domain given example sentences in that domain. As a solution we describe a method enlarging the vocabulary of a lan- guage model to an almost infinite size and capturing their context information. Espe- cially the new method is suitable for lan- guages in which words are not delimited by whitespace. We applied our method to a phoneme-to-text transcription task in Japanese and reduced about 10% of the er- rors in the results of an existing method. 1 Introduction The noisy channel model approach is being suc- cessfully applied to various natural language pro- cessing (NLP) tasks, such as speech recognition (Jelinek, 1985), spelling correction (Kernighan et al., 1990), machine translation (Brown et al., 1990), etc. In this approach an NLP system is composed of two modules: one is a task- dependent part (an acoustic model for speech recognition) which describes a relationship be- tween an input signal sequence and a word, the other is a language model (LM) which measures the likelihood of a sequence of words as a sen- tence in the language. Since the LM is a common part, its improvement augments the accuracies of all NLP systems based on a noisy channel model. Recently the main research focus of LM is shift- ing to the adaptation method, how to capture the characteristics of words and expressions in a tar- get domain. The standard adaptation method is to prepare a corpus in the application domain, count the frequencies of words and word sequences, and manually annotate new words with their input sig- nal sequences to be added to the vocabulary. It is now easy to gather machine-readable sentences in various domains because of the ease of publication and access via the Web (Kilgarriff and Grefen- stette, 2003). In addition, traditional machine- readable forms of medical reports or business re- ports are also available. When we need to develop an NLP system in various domains, there is a huge but unannotated corpus. For languages, such as Japanese and Chinese, in which the words are not delimited by whitespace, one encounters a word identification problem be- fore counting the frequencies of words and word sequences. To solve this problem one must have a good word segmenter in the domain of the corpus. The only robust and reliable word segmenter in the domain is, however, a word segmenter based on the statistics of the lexicons in the domain! Thus we are obliged to pay a high cost for …

Content truncated to 500 words