1 Computational Linguistics Ling 200 Spring 2006

2 Speech and language processing Computational Linguistics  use of computers to facilitate linguistic research Natural Language Processing  computer-natural language interface applications

3 Combines disciplines Linguistics  e.g. grammar engineering Electrical Engineering  e.g. speech recognition Computer science  e.g. machine translation Psychology  e.g. cognitive modeling

4 2 Minute question (part 1) List the specific language related skills HAL exhibits. In other words, list the different abilities the computer (HAL) must have to display human-like language?

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6 Today’s goals Convey:  some areas of research  some of the difficulties involved  some development strategies Provide examples of particular technologies as illustration

7 Computerized natural language speech recognition language understanding language generation speech synthesis

8 Other areas of interest searching  understanding search request  finding relevant documents  ordering by degree of relevance information extraction  retrieving information from documents data mining  discovering patterns and relationships in data

9...and still more topics machine translation   summarization grammar checking spell checking

10 Commonly used tools formal rule systems computational search algorithms formal logic probability theory machine learning techniques

11 Speech Recognition Demo Software Used: iListen from MacSpeech

12 What is Speech Recognition? Definition: Speech recognition turns acoustic input into strings of phonemes and then finds the best matching word in a database.  Can be built for open domain use, theoretically recognizing all possible strings of words e.g. dictation systems  Can also be built for a particular domain, recognizing small, finite sets of utterances e.g. automated call-centers.

13 Speech Recognition Acoustic Model First, the continuous speech signal is broken up into short segments. Segments are analyzed into features, which you can think of as quantitative versions of the phonetic features you learned in class. By comparing segments against internally stored phonological model, well matched phonemes are proposed for each segment End up with a list of most likely phoneme sequences.

14 Speech Recognition Language Model Sequences of phonemes are verified by comparing with a database of words and their likelihoods (in real time), and only actual words and phrases are accepted  [r ɛ kənajspič]  [r ɛ kənajspič] ‘recognize speech’  [r ɛ kənajspiš]  [r ɛ kənajspiš] ??‘recognize speesh’ *Fast speech: [z] -> [s] / _[s]

15 Problems Acoustic Model Recognizing different voice qualities as the same basic sounds. You can think of this as choosing the correct phoneme.  Phonemes sound different (allophones), depending on their environments. word position: /p/ --> [p h ] / #_ assimilation: /z/ --> [s] / _C [-voice] deletion: [s] --> ø / _[s]  “Three cats sit.” Speech signal is continuous and full of non-speech noise.

16 Problems Ambiguity Same or very similar sequence of phonemes can correspond to multiple words or phrases  Homophones Words  [dir] ‘deer’ ‘dear’ Phrases (remember there is no pause to separate word boundaries)  [r ɛ kənajspič] ‘recognize speech’  [r ɛ kənajspič] ‘wreck a nice beach’

17 Potential Fix Language Model Weight word/phrase interpretations (statistical language modeling) Lexical: Consider how often a word actually occurs.  [dir] ‘deer’ (50) ‘dear’ (215) Choose most frequent, in this case ‘dear’ Condition on context: Consider how often a word occurs within a particular context. I just shot a [dir]. (shot, a, dear) 1 (shot, a, deer) 10  In this case, ‘deer’ occurs more frequently in this environment, so we choose ‘deer’ as our interpretation.

18 Demo Training Data Matters Word and context frequencies are not just pulled from thin air. Frequencies are calculated (training)  From some collection of text (a corpus). Speech recognizers often train on a user’s s and documents, to better match the user’s lexical choice and phrase patterns. This training data helps decipher homophonous strings (strings that are acoustically ambiguous).

19 Demo 2 Training Data Matters I will attempt to utter the following phrase and iListen should transcribe my speech. It’s hard to…  [r ɛ kənajspič] ‘recognize speech’  [r ɛ kənajspič] ‘wreck a nice beach’

20 Demo 3 Linguist What if software is trained for a Computational Linguist?  Trained on 3 Wikipedia articles about various topics in Computational Linguistics  Which interpretation should we expect, based on words and phrases likely to be present in computational linguistics documents?  Results:Is hard to recognize speech New set the state  but is so bad and found a 544 is no sound better, even so it is etc is not really that bad so at and his exist listening to 89, Nancy of

21 Demo 4 Beach Bum What if software is trained for a Beach Bum?  Trained on 3 Wikipedia articles on beach topics.  Which interpretation should we expect, based on frequent words and phrases likely to be found in beach-related documents?  Results:It’s hard to wreck nice beach and

22 Language understanding morphology syntax semantics pragmatics discourse

23 "I made her duck.” I cooked waterfowl for her I cooked waterfowl belonging to her I created the (plaster?) duck she owns I caused her to quickly lower her head or body I waved my magic wand and turned her into undifferentiated waterfowl

24 Language generation “I'm sorry, Dave, I'm afraid I can't do that”  pragmatics: politeness indirect speech  morphology: contractions  discourse: reference (“that”)

25 Who/what is ELIZA?

26 Dialogue systems - issues HAL has complete understanding - How close are we to this? Eliza had no semantic understanding and only minimal syntactic knowledge dialogue systems: effective in limited domains like travel

27 Dialogue systems: demo [David] Chatbot website: 

28 2 minute question (part 2) Do you think that HAL quality computer communication is a reasonable expectation? Why or why not?