1. Integrated Stochastic Pronunciation Modeling Dong Wang Supervisors: Simon King, Joe Frankel, James Scobbie

2. Contents  Problems we are addressing  Previous research  Integrated stochastic pronunciation modeling  Current experimental results  Work plan

3. Problems we are addressing 1.Constructing a lexicon is time consuming. 2.Traditional lexicon-based triphone systems lack robustness to pronunciation variation in real speech. Linguistics-based lexica seldom considering real speech Deterministic decomposition from words to acoustic units, through lexica and decision tress

4. Previous research  Alternative pronunciation generation Utilize real speech to expand the lexicon.  Automatic lexicon generation Utilize real speech to create a lexicon.  Hidden sequence modeling (HSM) Build a probabilistic mapping from phonemes to context dependent phones.

5. Previous research Problems: 1. Linguistics-based lexica 2. determinate mapping

6. Integrated stochastic pronunciation modeling Integrated Stochastic Pronunciation Modeling (ISPM) Build a flexible three-layer architecture which represents pronunciation variation in probabilistic mappings, achieving better performance than traditional triphone-based systems. Focus on the grapheme-based ISPM system, eliminating human efforts on lexicon construction.

7. Integrated stochastic pronunciation modeling Grapheme-based ISPM

8. Integrated stochastic pronunciation modeling  Spelling simplification model (SSM) Map a letter string with regular pronunciation into a simple grapheme according to the context. e.g., EA->E Map a letter string with several pronunciations to simple graphemes, with appearance probability attached, e.g., OUGH->O (0.6) AF (0.4) Examining the transcription from the grapheme decoding against the reference transcription will help find the mapping.  Grapheme pronunciation model (GPM) The probabilistic mapping between the canonical layer and acoustic layer. LMs/decision trees/ANNs can all be examined here.

9. Integrated stochastic pronunciation modeling  Why graphemes? Simple relationship between word spellings and sub-word units helps generate baseforms for any words, so avoid human efforts on lexicon construction. It is easy to handle OOV words and reconstruct words from grapheme strings. Building and applying grapheme-based LMs will be simple. Internal composition of phonology rules and acoustic clues makes it suitable for some applications, such as spoken term detection and language identification.

10. Integrated stochastic pronunciation modeling  Direct grapheme ISPM Direct grapheme ISPM: SSM is a 1:1 mapping

11. Integrated stochastic pronunciation modeling  Hidden grapheme ISPM Hidden grapheme ISPM: SSM is a n:m mapping

12. Integrated stochastic pronunciation modeling  Training A divide-and-conquer approach, as in HSM, will be utilized for ISPM training. With this approach, SSM,GPM and AM are optimized iteratively and alternately within an EM framework, which ensures the process to converge to a local optimum. The acoustic units will be grown from a set of initial single-letter grapheme HMMs, as in the automatic lexicon generation approach.  Decoding The optimized ISPM will be used to expand searching graphs fed to the viterbi decoder. No changes are required in the decoder itself.  Implementation steps The SSM and GPM are well separated so can be designed/implemented respectively, and then are combined together. The SSM is relatively simpler therefore will be implmented first.

13. Integrated stochastic pronunciation modeling  The proposed ISPM will be evaluated on three tasks: Large vocabulary speech recognition (LVSR) Spoken term detection (STD) Language identification (LID) Simplest grapheme (NONO) Simple grapheme (SSM) Direct grapheme (GPM) Hidden grapheme (SSM+GPM) LVSR ★★★ ★★ ★ STD ★★ ★★ ★★ ★★ ★ LID ★★ Performance gain expectation from ISPM

14. Current experimental results  Large vocabulary speech recognition Training(h.)Development(h.)Evaluation(h.) WSJCAM014.90.651.00 RT04S103.91.401.66 Training vocTest vocLanguage model WSJCAM0 WSJ-5kWSJ 3-gram RT04S CMU+festiva l CMUAMI 3-gram WSJCAM0 for read speech and RT04S for spontaneous speech on the meeting domain Experiment settings for the LVSR task Data corpora for the LVSR task

15. Current experimental results Phoneme system(WER)Grapheme system(WER) WSJCAM011.3%15.8% RT04S44.5%54.5%  Large vocabulary speech recognition CI(WER)CD(WRE) Phoneme21.2%9.8% Grapheme48.4%13.0% Contribution of context dependent modeling Experimental results of the LVSR task

16. Current experimental results Conclusions The Grapheme-based system works usually worse than the phoneme-based one, especially in the RT04S task which is on the meeting domain, where 10% absolute performance degradation is observed. A grapheme-based system relies on context dependent modeling more than a phoneme-based system, and requires more Gaussian mixture components. State-tying questions that reflect phonological rules are helpful. Other experiments showed that manually-designed multi-letter graphemes do not help significantly.  Large vocabulary speech recognition Phoneme(WER)Grapheme(WER) Extended questions Grapheme(WER) Singleton questions 11.3%15.8%16.5% Contribution of phonology oriented questions to the grapheme system

17. Current experimental results  Spoken term detection sub-word lattice based architecture for STD

18. Current experimental results  Figure of Merit (FOM): average detection rate over the range [1,10] false alarms per hour.  Occurrence-weighted value (OCC) phonegrapheme FOM20.518.0 OCC0.440.34 ATWV0.250.16 WER44.5%54.5% STD performance on the RT04S task  Spoken term detection  Actual term-weighted value(ATWV)

19. Current experimental results  Spoken term detection A Grapheme-based STD systems is attractive because OOV words can be handled easily and the lattice search is efficient and simple. In our experiments the phoneme-based STD system works better. We suppose this because some unpopular terms are more difficult for the grapheme-based system to recognize. If similar ASR performance can be achieved, the grapheme-based system will outperform the phoneme-based one, as shown in the right figure.

20. Current experimental results  Spoken term detection We have demonstrated that in Spanish, which holds simple grapheme- phoneme relationship and achieves close ASR performance with phoneme and grapheme based systems, the grapheme-based STD system outperforms the phoneme-based one.

21. Current experimental results  Language identification parallel phone/grapheme recognizer architecture for LID

22. Current experimental results DER% phonegraphemePhone+grapheme unit likelihood35.632.127.9 sentence likelihood46.839.639.4  Language identification Globalphone is used for initial experiments, but we will move to NIST standard corpora. Detection error rate (DER), defined as the incorrect detection divided by total trials, is used as metric. Results on 3 seconds of speech within 4 languages are reported. Scores of whole sentences and those averaged over sub-word units as the ANN input are all tested.

23. Work plan  Phase I: Simple grapheme-based system 1. Finish the STD experiments with high-order LMs (by Jan.2008). 2. Finish the LID oriented tuning (by Nov.2007). 3. Apply powerful LMs to the LID task (by Jan.2008). 4. Finish the SSM design (by Jan.2008). 5. Apply the SSM on LVSR RTS04 and STD (by Feb.2008).  Phase II: Integrated stochastic pronunciation modeling 1. Finish the direct-grapheme architecture (GPM) design (by Jul.2008). 2. Test the direct-grapheme architecture on the LVSR RTS04 task (by Oct.2008). 3. Finish the hidden-grapheme architecture (GPM+SSM) (by Jan.2009). 4. Test the hidden-grapheme architecture on the LVSR RTS04 task (by Feb.2009).  Phase III: Applications based on ISPM 1. Finish the test on the STD task (by May 2009). 2. Finish the test on the LID task (by May 2009).