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Concept
Speech translation
Summary
Speech translation is the process by which conversational spoken phrases are instantly translated and spoken aloud in a second language. This differs from phrase translation, which is where the system only translates a fixed and finite set of phrases that have been manually entered into the system. Speech translation technology enables speakers of different languages to communicate. It thus is of tremendous value for humankind in terms of science, cross-cultural exchange and global business.
A speech translation system would typically integrate the following three software technologies:
automatic speech recognition (ASR), machine translation (MT) and voice synthesis (TTS).
The speaker of language A speaks into a microphone and the speech recognition module recognizes the utterance. It compares the input with a phonological model, consisting of a large corpus of speech data from multiple speakers.
The input is then converted into a string of words, using dictionary and grammar of language A, based on a massive corpus of text in language A.
The machine translation module then translates this string. Early systems replaced every word with a corresponding word in language B. Current systems do not use word-for-word translation, but rather take into account the entire context of the input to generate the appropriate translation. The generated translation utterance is sent to the speech synthesis module, which estimates the pronunciation and intonation matching the string of words based on a corpus of speech data in language B. Waveforms matching the text are selected from this database and the speech synthesis connects and outputs them.
In 1983, NEC Corporation demonstrated speech translation as a concept exhibit at the ITU Telecom World (Telecom '83).
In 1999, the C-Star-2 consortium demonstrated speech-to-speech translation of 5 languages including English, Japanese, Italian, Korean, and German.
Official source
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Machine translation
Machine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.