Machine Translation  Machine translation is of one of the earliest uses of AI  Two approaches:  Traditional approach using grammars, rewrite rules, and lexicons  May be shallow or deep translation  May translate directly between two languages, or from one language into interlingua and then into second language  Statistical machine translation

Shallow Translation  Transfer model: Keep a database of translation rules or examples. When rule matches, translate directly  Could operate on lexical, syntactic, or semantic level  Example: technical manuals (Siemens)  Doesn't deal with context

Deep Translation  One method uses language independent representation of information – interlingua  Three problems:  Knowledge representation  Parsing into that representation  Generation from representation  Alternate approach is directly from one language to another

Statistical Machine Translation  Translation model is learned from a bilingual corpus  One system:  Break the original sentences in phrases  Choose a corresponding phrase in the target language  Choose a permutation of the phrases  Select the most probable translation

Efficiency  Instead of examining all permutations (n!), use the concept of distortion  Distortion d i is the number of words that the phrase f i has moved with respect to f i-1, positive if moved to right, negative if moved to the left  Find a probability distribution for d  Each distortion is independent of the others

Efficiency (cont'd)  Still exponential over the number of phrases  Use beam search with a heuristic that estimates probability to find nearly-most-probably translation

Speech Recognition  Challenges:  Little segmentation, unlike written text  Coarticulation: sound at end of word runs into sound at beginning of next word  Homophones  Solutions:  Find acoustic model: P(sound i:t | word 1:t )  Find language model: P(word 1:t )  Use HMM and Viterbi algorithm

Acoustical Processing  Sample analog signal: sampling rate, quantization factor matter  Divide into frames  Extract features from each frame:  Use Fourier Transform to measure acoustic energy at about a dozen frequencies  Computer the mel frequency cepstral coefficient (mfcc) for each frequency  Yields thirteen features

Processing (cont'd)  Each phone has a onset, middle, and end  The phone models are strung together to form a pronunciation model for each word  Words can have a coarticulation model: “tomato” vs. “tomahto”  Language model can be an n-gram model learned from a corpus of text