Machine Translation from Speech
Handbook of Natural Language Processing and Machine Translation(2011)
摘要
This chapter describes approaches for translation from speech. Translation from speech presents two new issues. First, of
course, we must recognize the speech in the source language. Although speech recognition has improved considerably over the
last three decades, it is still far from being a solved problem. In the best of conditions, when the speech comes from high
quality, carefully enunciated speech, on common topics (such as speech read by a trained news broadcaster), the word error
rate is typically on the order of 5%. Humans can typically transcribe speech like this with less than 1% disagreement between
annotators, so even this best number is still far worse than human performance. However, the task gets much harder when anything
changes from this ideal condition. Some of the conditions that cause higher error rate are, if the topic is somewhat unusual,
or the speakers are not reading so that their speech is more spontaneous, or if the speakers have an accent or are speaking
a dialect, or if there is any acoustic degradation, such as noise or reverberation. In these cases, the word error can increase
significantly to 20%, 30%, or higher. Accordingly, most of this chapter discusses techniques for improving speech recognition
accuracy, while one section discusses techniques for integrating speech recognition with translation.
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关键词
speech recognition,machine translation,word error rate,error rate,human performance
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