Psych 156A/ Ling 150: Psychology of Language Learning Lecture 4 Words in Fluent Speech Announcements Homework 1 is due today by the end of class today Homework 2 available online, due 2/10/09 (after the midterm) Computational Problem Computational Problem Divide spoken speech into individual words Divide spoken speech into individual words tu@D kQ@s lbija@ndD ga@blInsI@ti tu@ to tu@D kQ@s lbija@ndD ga@blInsI@ti D the kQ@s l castle bija@nd D ga@blIn beyond the goblin sI@ti city 1 Word Segmentation One task faced by all language learners is the segmentation of fluent speech into words. This process is particularly difficult because word boundaries in fluent speech are marked inconsistently by discrete acoustic events such as pauses it is not clear what information is used by infants to discover word boundaries there is no invariant cue to word boundaries present in all languages. - Saffran, Aslin, & Newport (1996) Statistical Information Available Maybe infants are sensitive to the statistical patterns contained in sequences of sounds. Over a corpus of speech there are measurable statistical regularities that distinguish recurring sound sequences that comprise words from the more accidental sound sequences that occur across word boundaries. - Saffran, Aslin, & Newport (1996) to the castle beyond the goblin city Statistical Information Available Maybe infants are sensitive to the statistical patterns contained in sequences of sounds. Over a corpus of speech there are measurable statistical regularities that distinguish recurring sound sequences that comprise words from the more accidental sound sequences that occur across word boundaries. - Saffran, Aslin, & Newport (1996) Statistical regularity: ca + stle is a common sound sequence Statistical Information Available Maybe infants are sensitive to the statistical patterns contained in sequences of sounds. Over a corpus of speech there are measurable statistical regularities that distinguish recurring sound sequences that comprise words from the more accidental sound sequences that occur across word boundaries. - Saffran, Aslin, & Newport (1996) No regularity: stle + be is an accidental sound sequence to the castle beyond the goblin city to the castle beyond the goblin city word boundary 2 Transitional Probability Within a language, the transitional probability from one sound to the next will generally be highest when the two sounds follow one another in a word, whereas transitional probabilities spanning a word boundary will be relatively low. - Saffran, Aslin, & Newport (1996) Transitional Probability = Conditional Probability TrProb(AB) = Prob( B | A) Transitional probability of sequence AB is the conditional probability of B, given that A has been encountered. TrProb( gob lin ) = Prob( lin | gob ) Read as the probability of lin , given that gob has just been encountered Transitional Probability Within a language, the transitional probability from one sound to the next will generally be highest when the two sounds follow one another in a word, whereas transitional probabilities spanning a word boundary will be relatively low. - Saffran, Aslin, & Newport (1996) Transitional Probability = Conditional Probability TrProb( gob lin ) = Prob( lin | gob ) Example of how to calculate TrProb: gob ble, bler, bledygook, let, lin, stopper (6 options for what could follow gob ) TrProb( gob lin ) = Prob( lin | gob ) = 1/6 Transitional Probability Within a language, the transitional probability from one sound to the next will generally be highest when the two sounds follow one another in a word, whereas transitional probabilities spanning a word boundary will be relatively low. - Saffran, Aslin, & Newport (1996) Idea: Prob( stle | ca ) = high Why? ca is usually followed by stle Transitional Probability Within a language, the transitional probability from one sound to the next will generally be highest when the two sounds follow one another in a word, whereas transitional probabilities spanning a word boundary will be relatively low. - Saffran, Aslin, & Newport (1996) Idea: Prob( be | stle ) = lower Why? stle is not usually followed by be to the castle beyond the goblin city to the castle beyond the goblin city word boundary 3 Transitional Probability Within a language, the transitional probability from one sound to the next will generally be highest when the two sounds follow one another in a word, whereas transitional probabilities spanning a word boundary will be relatively low. - Saffran, Aslin, & Newport (1996) Prob( yond | be ) = higher Why? be is commonly followed by yond , among other options Transitional Probability Within a language, the transitional probability from one sound to the next will generally be highest when the two sounds follow one another in a word, whereas transitional probabilities spanning a word boundary will be relatively low. - Saffran, Aslin, & Newport (1996) Prob( be | stle ) < Prob( stle | ca ) Prob( be | stle ) < Prob( yond | be ) to the castle beyond the goblin city to the castle beyond the goblin city TrProb learner posits word boundary here, at the minimum of the TrProbs Important: doesn t matter what the probability actually is, so long as it s a minimum when compared to the probabilities surrounding it 8-month-old statistical learning Saffran, Aslin, & Newport 1996 Familiarization-Preference Procedure (Jusczyk & Aslin 1995) Habituation: Infants exposed to auditory material that serves as potential learning experience Test stimuli (tested immediately after familiarization): (familiar) Items contained within auditory material (novel) Items not contained within auditory material, but which are nonetheless highly similar to that material 8-month-old statistical learning Saffran, Aslin, & Newport 1996 Familiarization-Preference Procedure (Jusczyk & Aslin 1995) Measure of infants response: Infants control duration of each test trial by their sustained visual fixation on a blinking light. Idea: If infants have extracted information (based on transitional probabilities), then they will have different looking times for the different test stimuli. 4 Artificial Language Saffran, Aslin, & Newport 1996 4 made-up words with 3 syllables each Condition A: tupiro, golabu, bidaku, padoti Condition B: dapiku, tilado, burobi, pagotu Artificial Language Saffran, Aslin, & Newport 1996 Infants were familiarized with a sequence of these words generated by speech synthesizer for 2 minutes. Speaker s voice was female and intonation was monotone. There were no acoustic indicators of word boundaries. Sample speech: tu pi ro go la bu bi da ku pa do ti go la bu tu pi ro pa do ti Artificial Language Saffran, Aslin, & Newport 1996 The only cues to word boundaries were the transitional probabilities between syllables. Within words, transitional probability of syllables = 1.0 Across word boundaries, transitional probability of syllables = 0.33 Artificial Language Saffran, Aslin, & Newport 1996 The only cues to word boundaries were the transitional probabilities between syllables. Within words, transitional probability of syllables = 1.0 Across word boundaries, transitional probability of syllables = 0.33 TrProb( tu pi ) = 1.0 tu pi ro go la bu bi da ku pa do ti go la bu tu pi ro pa do ti tu pi ro go la bu bi da ku pa do ti go la bu tu pi ro pa do ti 5 Artificial Language Saffran, Aslin, & Newport 1996 The only cues to word boundaries were the transitional probabilities between syllables. Within words, transitional probability of syllables = 1.0 Across word boundaries, transitional probability of syllables = 0.33 TrProb( tu pi ) = 1.0 = TrProb( go la ), TrProb( pa do ) Artificial Language Saffran, Aslin, & Newport 1996 The only cues to word boundaries were the transitional probabilities between syllables. Within words, transitional probability of syllables = 1.0 Across word boundaries, transitional probability of syllables = 0.33 TrProb( ro go ) < 1.0 (0.3333 ) tu pi ro go la bu bi da ku pa do ti go la bu tu pi ro pa do ti tu pi ro go la bu bi da ku pa do ti go la bu tu pi ro pa do ti Artificial Language Saffran, Aslin, & Newport 1996 The only cues to word boundaries were the transitional probabilities between syllables. Within words, transitional probability of syllables = 1.0 Across word boundaries, transitional probability of syllables = 0.33 TrProb( ro go ), TrProb( ro pa ) = 0.3333 < 1.0 = TrPrb( pi ro ), TrProb ( go la ), TrProb( pa do ) Testing Infant Sensitivity Saffran, Aslin, & Newport 1996 Expt 1, test trial: Each infant presented with repetitions of 1 of 4 words 2 were real words (ex: tupiro, golabu) tu pi ro go la bu bi da ku pa do ti go la bu tu pi ro pa do ti word boundary word boundary 2 were fake words whose syllables were jumbled up (ex: ropitu, bulago) tu pi ro go la bu bi da ku pa do ti go la bu tu pi ro pa do ti 6 Testing Infant Sensitivity Saffran, Aslin, & Newport 1996 Expt 1, test trial: Each infant presented with repetitions of 1 of 4 words 2 were real words (ex: tupiro, golabu) Expt 1, results: Testing Infant Sensitivity Saffran, Aslin, & Newport 1996 Infants listened longer to novel items (non-words) (7.97 seconds for real words, 8.85 seconds for non-words) Implication: Infants noticed the difference between real words and non-words from the artificial language after only 2 minutes of listening time! 2 were fake words whose syllables were jum...