Retrieving Spoken Information by Combining Multiple Speech Transcription Methods

1. Retrieving Spoken Information by Combining Multiple Speech Transcription Methods Jonathan Mamou Joint work with Ron Hoory, David Carmel, Yosi Mass, Bhuvana Ramabhadran, Benjamin Sznajder

2. Motivation Spoken data is everywhere! Broadcasts News Conference Meetings Call Center Surveillance & Security Speech Technologies Seminar 2008

3. IR Tasks on Speech Data • Spoken Document Retrieval (SDR) • Traditional search engine approach: find spoken documents relevant to a query. • Spoken Term Detection (STD) • Detect occurrences of a phrase in spoken documents. • NIST STD evaluation Speech Technologies Seminar 2008

4. Approaches for Speech Information Retrieval • Keyword spotting • Based on direct detection of a predefined set of keywords in the speech data • Build an index out of automatic transcription output • Based on full transcription of the audio and indexing of the transcription process output • This is the approach we are using • Part of this work has been done in the framework of SAPIR, an EU FP6 project of Search in Audiovisual Content using P2P Speech Technologies Seminar 2008

5. Acoustic model Language model Vocabulary Overview Speech data Automatic Speech Recognition Index Query Search Engine Ranked Results Speech Technologies Seminar 2008

6. Why is it different from classic text IR? • The classic text IR based solution would be an indexing and search of 1-best word transcript. • However, two main issues can arise during the transcription of the speech data: • Errors (substitutions, deletions, insertions) can occur during the transcription • Out-of-vocabulary (OOV) terms can be present in the spoken data and in the query • OOV words are missing words from the ASR system vocabulary • They are replaced in the output transcript by alternatives that are probable, given the acoustic model, vocabulary and language model of the ASR system. • e.g., TALIBAN  TELL A BAND • Over 10% of user queries can be OOV terms (especially named entities) Speech Technologies Seminar 2008

7. Influence of the WER on the Retrieval • Substitutions and deletions reflect the fact that a term “appearing” in the speech signal is not recognized • Impact on the recall of the search (i.e., fraction of the documents relevant to the query that are successfully retrieved) • Substitutions and insertions reflect the fact that a term which is not part of the speech signal appears in the transcript • Impact on the precision of the search (i.e., fraction of the retrieved documents that are relevant to the query) • These issues may dramatically affect the effectiveness of the retrieval and prevent the “naïve” search engine from retrieving the information Speech Technologies Seminar 2008

8. Technical Approach • We have developed algorithms • to improve search effectiveness in the presence of errors, • to allow OOV queries. • Indexing of the Word Confusion Network (WCN) including word alternatives and corresponding confidences, for IV terms. • Phonetic indexing and fuzzy search. Speech Technologies Seminar 2008

9. Retrieval Model

10. Word Search • We index Word Confusion Network (WCN) [Mangu et al., 2000] • It is a compact representation of a word lattice: the different word hypotheses that appear at a same time are aligned. • A vertex is associated with a timestamp. • An edge is labeled with • a word hypothesis, • its posterior probability: the probability of the word given the signal. Speech Technologies Seminar 2008

11. A fragment of WCN glasses 27% graphic 22% have 61% impressions 19 % screen 99% … … on 100% my 100% graphics 13% seen 1% and 39% interested 9% impresses 7 % grass 3% Speech Technologies Seminar 2008

12. Improving Retrieval Effectiveness using WCNs • Recall is enhanced by expanding the 1-best transcript by extra words, taken from the other alternatives provided by the WCN. • These alternatives may have been spoken but were not the top choice of the ASR. • However, such an expansion will probably decrease the precision! • Using an intelligent ranking model, we can improve the mean average precision (MAP) of the search. • Average precision is average of precisions computed after truncating the list of results after each of the relevant documents in turn. • MAP emphasizes returning more relevant documents earlier. Speech Technologies Seminar 2008

13. Improving Retrieval Effectiveness using WCNs • We exploit two pieces of information provided by WCN concerning the occurrences of a term to improve our ranking model: • The posterior probability of the hypothesis given the signal, • The rank of the hypothesis among the other alternatives. Speech Technologies Seminar 2008

14. Posterior Probability of the Hypothesis, Confidence Level • The posterior probability of the hypothesis given the signal reflects the confidence of the ASR in the hypothesis. • The retrieval process will boost documents for which the query term occurs with higher probability. • We denote by Pr(t|o,D) the posterior probability of a term t at offset o in the WCN of a document D. Speech Technologies Seminar 2008

15. Rank of the Hypothesis, Relative Importance • The rank of the hypothesis among other alternatives reflects the importance of the term relatively to other alternatives. • A document containing a query term that is ranked higher, should be preferred over a document where the same term is ranked lower. • We denote by rank(t|o,D) the rank of a term t at the offset o in the WCN of a document D. • A boosting vectorB=(B1,…,Bl) associates a boosting factor to each rank of the different hypotheses. Speech Technologies Seminar 2008

16. Scoring • Our scoring is based on Vector Space Model (VSM) [Salton and McGill, 1986] • It is an algebraic model for representing documents as vectors of words. • Each dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is its tfidf. • Relevance ranking of documents can be calculated by comparing the cosine of the angles between each document vector and the original query vector where the query is represented as same kind of vector as the documents. d1 d2 q Speech Technologies Seminar 2008

17. Scoring • Term frequency – Inverse document frequency (tfidf) • This weight is a statistical measure used to evaluate how important a word is to a document in a corpus. • The importance increases proportionally to the number of times a word appears in the document (term frequency - tf) but is offset by the frequency of the word in the corpus (inverse document frequency - idf). Speech Technologies Seminar 2008

18. Term Frequency • The term frequency is evaluated by summing the posterior probabilities of all the occurrences of the term over the document. • The term frequency is boosted by the rank of the term among the other hypotheses. occ(t,D) is the sequence of all the occurrences of t in D. Speech Technologies Seminar 2008

19. Phonetic Search • Different kinds of phonetic transcripts: • Sub-word decoding [Siohan and Bacchiani, 2005] • Sub-word representation of automatic 1-best word transcript • Sub-word can be word-fragment, syllable, phone • Sub-word transcripts have high error rate • Phonetic transcription cannot be an alternative to word transcripts especially for in-vocabulary (IV) search. • That is the reason why we need to combine word transcripts with phonetic transcripts. Speech Technologies Seminar 2008

20. Phonetic Search • Relevant to IV and OOV search • N-gram or sub-word based indexing • Retrieval approaches • Exact search • High precision but low recall • Fuzzy search • It improves recall while decreases precision • Using an intelligent ranking model, we can improve the mean average precision of the search. • Based on Edit distance on pronunciations • We have implemented a fail-fast dynamic algorithm for computing it Speech Technologies Seminar 2008

21. Scoring • Our scoring model extends TFIDF. • Let’s consider a query that is represented by the phonetic pronunciation ph. • sim(ph,ph’) is the edit distance based similarity between two phonetic pronunciations ph and ph’. • Term frequency: • Document frequency: N is the number of documents in the corpus. Speech Technologies Seminar 2008

22. Phonetic Query Expansion • Compensate for • OOV • spelling variations • Each query term is converted to its phonetic pronunciations using joint maximum entropy N-gram model [Chen, 2003]. • Each pronunciation is associated with a score that reflects the probability of this pronunciation normalized by the probability of the best pronunciation, given the spelling. Speech Technologies Seminar 2008

23. Phonetic Query Expansion • Let’s consider a query term t that is expanded to (ph1,s1), …, (phm,sm) where phi is a pronunciation and si its associated score. • The score of t in D is given by the aggregation of the scores of the search on D of the pronunciations phi weighted by their score si Speech Technologies Seminar 2008

24. Combination of word search with phonetic search • Using the Threshold Algorithm [Fagin, 1996] • Merging result lists of documents returned respectively by word and phonetic search, ordered according to their score • Using inverted indices with Boolean Constraints • Merging posting lists extracted from inverted indices (word and phonetic), ordered by the document identifiers, according to Boolean constraints • Based on query rewriting to combine word and phonetic parts of the original query Speech Technologies Seminar 2008

25. Experiments

26. Experimental Setup • 2236 calls made to the IBM internal customer support service. • The calls deal with a large range of software and hardware problems. • The average length of a call is 18 minutes. Speech Technologies Seminar 2008

27. Precision and Recall vs. WER • As expected, indexing all WCN candidates improve Recall while reduce Precision • Recall/Precision are both decreasedwith higher WER Speech Technologies Seminar 2008

28. Experiments with several retrieval strategies over WCN • 1-best WCN TF • Index: 1-best transcript obtained from WCN - Ranking: classic tf-idf • All WCN TF • Index: all the WCN hypotheses - Ranking: classic tf-idf • 1-best WCN CL • Index: 1-best transcript obtained from WCN - Ranking: confidence levels • All WCN CL • Index: all the WCN hypotheses - Ranking: confidence levels • ALL WCN CL boost • Index: all the WCN hypotheses - Ranking: confidence levels and rank among the other hypotheses Speech Technologies Seminar 2008

29. MAP vs. WER • Using confidence level information provides significant contribution. • all WCN CL boost always outperforms the other models, especially for high WER. Speech Technologies Seminar 2008

30. Experimental Setup • Data set provided by NIST for the STD evaluation • 3 hours of broadcast news • We built three different indices • Word: word index on the WCN • WordPhone: a phonetic index of the phonetic representation of the 1-best word decoding • Phone: a phonetic index of the 1-best word-fragment decoding • For phonetic retrieval, we compared two different search methods: exact and fuzzy match. Speech Technologies Seminar 2008

31. MAP of Phonetic Query Expansion for OOV search • MAP of phonetic retrieval improves by up to 7.5% with query expansion in respect to baseline search approaches. Speech Technologies Seminar 2008

32. MAP for Hybrid Search • Queries combine IV and OOV terms under different query semantics • Improvement of merge approach with respect to word and phonetic approaches Speech Technologies Seminar 2008

33. Conclusions • The Approach • Word-based approach suffers from limited vocabulary of the recognition system. • Phonetic-based approach suffers from lower accuracy. • Our spoken information retrieval system combines both approaches • Recall and MAP are significantly improved by searching • all the hypotheses provided by the WCN • in phonetic transcripts • This approach received the highest overall ranking for US English speech data in the last NIST Spoken Term Detection evaluation (December 2006). Speech Technologies Seminar 2008

34. References • Spoken Document Retrieval from Call-Center Conversations, Jonathan Mamou, David Carmel, Ron Hoory, SIGIR 2006 • Vocabulary Independent Spoken Term Detection, Jonathan Mamou, Bhuvana Ramabhadran, Olivier Siohan, SIGIR 2007 • Audio-visual content analysis in P2P: the SAPIR approach, Walter Allasia, Francesco Gallo, Fabrizio Falchi, Mouna Kacimi, Aaron Kaplan, Jonathan Mamou, Yosi Mass, Nicola Orio, Workshop on Automated Information Extraction in Media Production, DEXA 2008 • Combination of Multiple Speech Transcription Methods for Vocabulary Independent Search, Jonathan Mamou, Yosi Mass, Bhuvana Ramabhadran, Benjamin Sznajder, Search in Spontaneous Conversational Speech Workshop, SIGIR 2008 • Phonetic Query Expansion for Spoken Document Retrieval, Jonathan Mamou, Bhuvana Ramabhadran, Interspeech 2008 Speech Technologies Seminar 2008

35. Thank you!