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We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.

Forensic Speaker Recognition T here has long been a desire to be able to identify a person on the basis of his or her voice. For many years, judges, lawyers, detectives, and law enforcement agencies have wanted to use forensic voice authentication to investigate a suspect or to confirm a judgment of guilt or innocence [3] [35]. Challenges, realities, and cautions regarding the use of speaker recognition applied to forensic-quality samples are presented. Identifying a voice using forensic-quality samples is generally a challenging task for automatic, semiautomatic, and humanbased methods. The speech samples being compared may be recorded in different situations; e.g., one sample could be a yelling over the telephone, whereas the other might be a whisper in an interview room. A speaker could be disguising his or her voice, ill, or under the influence of drugs, alcohol, or stress in one or more of the samples. The speech samples will most likely contain noise, may be very short, and may not contain enough relevant speech material for comparative purposes. Each of these variables, in addition to the known variability of speech in general, makes reliable discrimination of speakers a complicated and daunting task. Although the scientific basis of authentication of a person by using his or her voice has been questioned by researchers (e.g., by scientists in 1970 [4], British academic phoneticians in 1983 [5], and the French speech communication community from 1990 to today [6]), there is a perception among the

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