As it is impossible to have a lexicon with complete coverage, and a high proportion of unknown words are proper names, this paper addresses the issue of automatically finding pronunciations of unseen proper names in US English. Proper names, especially in the US, may come from a large range of ethnic backgrounds. We present a model and results showing that including ethnic origin of words in a statistical model can improve pronunciation results.

This is "sentence 782" extracted from the book "The New C Standard: An Economic and Cultural Commentary"

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This thesis describes MultiSphinx, a concurrent architecture for scalable, low-latency automatic speech recognition. We first consider the problem of constructing a universal “core ” speech recognizer on top of which domain and task specific adaptation layers can be constructed. We then show that when this problem is restricted to that of expanding the search space from a “core ” vocabulary to a superset of this vocabulary across multiple passes of search, it allows us to effectively “factor ” a recognizer into components of roughly equal complexity. We present simple but effective algorithms for constructing the reduced vocabulary and associated statistical language model from an existing system. Finally, we describe the MultiSphinx decoder architecture, which allows multiple passes of recognition to operate concurrently and incrementally, either in multiple threads in the same process, or across multiple processes on separate machines, and which allows the best possible partial results, including confidence scores, to be obtained at any time during the recognition process. Acknowledgments v vi This thesis would not be possible without the support of all the friends, family, faculty, and colleagues who steered, encouraged, and supported me all the way. First and foremost, I am deeply grateful to my advisor, Dr. Alexander I. Rudnicky, for his support through the MLT and PhD programs here at CMU, and