Survey of Approaches to Information Retrieval of Speech Messages

1. Survey of Approaches to Information Retrieval of Speech Messages Kenney NgSpoken Language Systems GroupLaboratory for Computer ScienceMassachusetts Institute of TechnologyFebruary 16 , 1996DRAFT 報告人：朱惠銘

2. Survey of Approaches to Information Retrieval of Speech Messages • Introduction • Information Retrieval • Text Retrieval • Differences between text and speech media • Information Retrieval of Speech Messages

3. 1 Introduction • Process, organize, and analyze the data. • Present the data in human usable form. • Find the “interesting” piece of information efficiently. • Increasingly large portions in spoken language information： • recorded speech messages • radio and television broadcasts • Development of automatic methods.

4. 2 Information Retrieval • 2.1 Definition • The representation, storage , organization and accessing of information items. • Return the best match of the “request” provided by the user. • There is no restriction on the type of documents. • Text Retrieval , Document Retrieval • Image Retrieval , Speech Retrieval • Multi-media Retrieval

5. 2.2 Information Retrieval vs. Database Retrieval

6. 2.3 Component Processes Creating document representations (indexing) Creating request representations (query formation) Comparing representations(retrieval) Evaluating retrieved documents(relevance feedback)

7. 2.3 Component Processes (cont.)Performance • Recall • The fraction of all the relevant documents in the entire collection that are retrieved in response to a query. • Precision • The fraction of the retrieved documents that are relevant. • Average precision • The precision values obtained at each new relevant document in the ranked output for an individual query are averaged.

8. 3 Text Retrieval • 3.1 Indexing and Document Representation • 3.2 Query Formation • 3.3 Matching Query and Document Representation

9. 3.1 Indexing and Document Representation • Terms and Keywords • A list of words extracted from the full text document. • Construct a Stop list to remove the useless words. • Under the usage of synonyms • Construct a dictionary structure to modify • To replace each word in one class • Tradeoff exists between normalization and discrimination in the indexing process

10. Index Term Weighting • Term frequency • The frequency of occurrence of each term in the document • For term tk in document di

11. Index Term Weighting • Inverse document frequency • Approach of weighting each term inversely proportional to the number of documents in which the term occurs. • For term tkN is the total number of documentsntk is the number of documents with term tk

12. Index Term Weighting • Weights to terms • Terms that occur frequently in particular documents but rarely in the overall collection should receive a large weight.

13. 3.2 Query Formation • Relevance Feedback • The IR system automatically modifies a query based on user feedback about documents retrieved in an initial run.

14. 3.2 Query Formation • Extracting from a user request a representation of its content. • The indexing method also applicable to query formation.

15. 3.3 Matching Query and Document Representations • Boolean Model, Extended Boolean Model • Vector Space Model • Probabilistic Models

16. Boolean Model • Document representation • Binary value variable • True: the term is present in the document • False: the term is absent in the document • The document can be represented in a binary vector • Query • Boolean query : AND, OR and NOT • Matching function • Standard rule of Boolean logic • If the document representation satisfy the query expression then that document matches the query

17. Extended Boolean Model • The retrieval decision of the Boolean Model may be too harsh. • The extended boolean model • This is maximal for a document contain all the terms and decreases the numbers of matching term decreases.

18. Extended Boolean Model • For the OR query • This is minimal for a document that contains none of the terms and increases as the number of matching terms increases. • The variable p is a constant in the range 1≤p≤∞ that is determined empirically;it is typically in the range 2≤p≤5.

19. Vector Space Model • Documents and queries are represented as vector in a K-dimensional space • K is the number of indexing terms.

20. Probabilistic Models • Baye’s Decision Rule • The probability that the document d is relevant to the query q denotes • The probability that the document d is non-relevant to the query q denotes • Cr is the cost of retrieving a non-relevant document • Cn is the cost of not retrieving a relevant document • The expected cost of retrieving a extraneous document is

21. Probabilistic Models (cont.) • How to compute the and which are posteriorprobabilities? • Base on Bayes’ Rule • , are the prioriprobabilities of relevance and non-relevance of a document. • , are the likelihoods or class conditionalprobabilities.

22. Probabilistic Models (cont.) • Now we have to estimate and

23. Probabilistic Models (cont.) • Assumptions • The document vectors are binary, indicating the presence or absence of each indexing term. • Each term has a binomial distribution. • There are no interactions between the terms.

24. Probabilistic Models (cont.)

25. Probabilistic Models (cont.) • wk is the same as the relevance weight of kth index term • Assume pk a constant value : 0.5 • qk overall frequency : nk/N

26. 4 Differences between text and speech media • Speech is a richer and more expressive medium than text. (mood, tone) • Robustness of the retrieval models to noise or errors in transcription. • How to accurately extract and represent the contents of a speech message in a form that can be efficiently stored and searched.

27. 5 Information Retrieval of Speech Messages • Speech Message Retrieval • Large Vocabulary Word Recognition Approach • Sub-Word Unit Approach • Word Spotting Approaches • Speech Message Classification and Sorting • Topic Identifications • Topic Spotting • Topic Clustering

28. Large Vocabulary Word Recognition Approach • Suggested by CMU in Information digital video library project. • A user can interact with the text retrieval system to obtain video clips stored in the library that are relevant to his request. Sound trackof video Large vocabularyspeech recognizer Textualtranscript Full-text informationretrieval system Natural languageunderstanding

29. Sub-Word Unit Approach • Syllabic Units • Phonetic Units

30. Syllabic Units • VCV-features • Sub-word units consist of a maximum sequence of consonants enclosed between two maximum sequences of vowels. • eg: INFORMATION has INFO,ORMA,ATIO vcv-features • Take subset of these features as the indexing terms.

31. Syllabic Units • Criteria • Occur frequently enough for a reliable acoustic model to be trained for it. • Not occur so frequently that its ability to discriminate between different messages is poor. • Process query VCV-features tf*idf weight Document representation Cosine similarity function Document with highest score

32. Syllabic Units • Major problem • The acoustic confusability of VCV-feature based approach is not taken into account during the selection of indexing features

33. Phonetic Units • Using variable length phone sequences as indexing feature. • These features can be viewed as “pseudo -word” and were shown to be useful for detecting or spotting topics in recorded military radio broadcasts. • An automatic procedure based on “digital trees” is used to search the possible subsequences • A Hidden Markov Model (HMM) phone recognizer with 52 monophone models is used to process the speech • More domain independent than a word based system.

34. Word Spotting Approaches • Between the simple phonetic and the complex large-vocabulary recognition. • Two different ways that word spotting has been used. • 1. Small, fixed number of keywords are selected a priori for both recognition and indexing. • 2. The speech messages in the collection are processed and stored in a form (e.g. phone lattice) that allows arbitrary keywords to be searched for after they are specified by the user.

35. Speech Message Classification and Sorting • Topic Identifications (1) • K keywords • nk is the binary value indicating the presence or absence of keyword wk. • Finding that topic Ti which maximum the score Si

36. Speech Message Classification and Sorting • Topic Identifications (1) • If there are 6 topics , top scoring 40 words each,total 240 keywords . • These keywords used on the text transcriptions of the speech messages 82.4% classification accuracy achieved • If a genetic algorithm used to reduced the number of keywords down to 126 with a small drop in classification performance to 78.2% .

37. Topic Identifications (2) • The topic dependent unigram language models • K is the number of keywords in the indexing vocabulary • nk is the number of times keyword wk occurs in the speech message • p( wk | Ti ) is the unigram or occurrence probability of keyword wk in the set of class Ti message.

38. Topic Identifications (2)

39. Topic Identifications (3) • The length normalized topic score • N is the total number of words in speech message • K is the number of keywords in the indexing vocabulary • nk is the number of times keyword wk occurs in the speech message • p( wk | Ti ) is the unigram or occurrence probability of keyword wk in the set of class Ti message.

40. Topic Identifications (3) • 750 keywords • Classification accuracy is 74.6%

41. Topic Identifications (4) • The topic model is extended to a mixture of multinomial • M is the number of multinomial model components • Πmis the weight of the mth multinomial component • K is the number of keywords in the indexing vocabulary • nk is the number of times keyword wk occurs in the speech message • p( wk | Ti ) is the unigram or occurrence probability of keyword wk in the set of class Ti message.

42. Topic Identifications (4) • Experiments indicate that the more complex models do not perform as well as the simple single mixture model.

43. Topic Spotting (1) • “usefulness” measure how discriminating the word is for the topic. • and are the probabilities of detecting the keyword in the topic and unwanted • This measure select words that occur often in the topic and have high discriminability .

44. Topic Spotting (2) • Performed by accumulating over a window of speech (typically 60 seconds) • The log likelihood ratio of the detected keywords to produce a topic score for that region of the speech message.

45. Topic Spotting (2) • Try to capture dependencies between the keywords are examined. • w represent the vector of keywords • is the coefficient of model . • Their experiments show that using a carefully chosen log-linear model can give topic spotting performance that is better than using the basic model that assumes keyword independence

46. Topic Clustering • Try to discover structure or relationships between messages in a collection. • The clustering process • Tokenization • Similarity computation • Clustering

47. Topic Clustering (cont.) • Tokenization to come up with a suitable representation of the speech message which can be used in the next two steps. • Similarityit needs to compare every pair of messages,N-gram model is used. • Clusteringusing hierarchical tree clustering or nearest neighbor classification. • Work well under true transcription texts figure of merit (FOM) 90% rates • Using speech input is worse than texts, it down to 70% FOM using recognition output, unigram language models and tree-based clustering.

48. Thanks for all