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Integrated Stochastic Pronunciation Modeling
Dong Wang
Supervisors: Simon King, Joe Frankel, James Scobbie
Contents
Problems we are addressing Previous research Integrated stochastic pronunciation modeling Current experimental results Work plan
Problems we are addressing
1. Constructing a lexicon is time consuming.2. Traditional lexicon-based triphone systems lack robustness to pronunciatio
n variation in real speech. Linguistics-based lexica seldom considering real speech Deterministic decomposition from words to acoustic units, through lexica and decis
ion tress
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.
Previous research
Problems:1. Linguistics-based lexica2. determinate mapping
Integrated stochastic pronunciation modeling
Integrated Stochastic Pronunciation Modeling (ISPM) Build a flexible three-layer architecture which represents pronunciati
on variation in probabilistic mappings, achieving better performance than traditional triphone-based systems.
Focus on the grapheme-based ISPM system, eliminating human effo
rts on lexicon construction.
Integrated stochastic pronunciation modeling
Grapheme-based ISPM
Integrated stochastic pronunciation modeling
Spelling simplification model (SSM) Map a letter string with regular pronunciation into a simple grapheme accor
ding to the context. e.g., EA->E Map a letter string with several pronunciations to simple graphemes, with ap
pearance probability attached, e.g., OUGH->O (0.6) AF (0.4) Examining the transcription from the grapheme decoding against the refere
nce transcription will help find the mapping.
Grapheme pronunciation model (GPM) The probabilistic mapping between the canonical layer and acoustic layer. LMs/decisi
on trees/ANNs can all be examined here.
Integrated stochastic pronunciation modeling
Why graphemes? Simple relationship between word spellings and sub-word units help
s 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 la
nguage identification.
Integrated stochastic pronunciation modeling
Direct grapheme ISPM
Direct grapheme ISPM: SSM is a 1:1 mapping
Integrated stochastic pronunciation modeling
Hidden grapheme ISPM
Hidden grapheme ISPM: SSM is a n:m mapping
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 decode
r. 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.
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
Current experimental results
Large vocabulary speech recognition
Training(h.) Development(h.) Evaluation(h.)
WSJCAM0 14.9 0.65 1.00
RT04S 103.9 1.40 1.66
Training voc Test voc Language model
WSJCAM0 WSJCAM0 WSJ-5k WSJ 3-gram
RT04S CMU+festival
CMU AMI 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
Current experimental results
Phoneme system(WER) Grapheme system(WER)
WSJCAM0 11.3% 15.8%
RT04S 44.5% 54.5%
Large vocabulary speech recognition
CI(WER) CD(WRE)
Phoneme 21.2% 9.8%
Grapheme 48.4% 13.0%
Contribution of context dependent modeling
Experimental results of the LVSR task
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 d
o 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
Current experimental results
Spoken term detection
sub-word lattice based architecture for STD
Current experimental results
Figure of Merit (FOM): average detection rate over the range [1,10] false alarms per hour.
Occurrence-weighted value (OCC)phone grapheme
FOM 20.5 18.0
OCC 0.44 0.34
ATWV 0.25 0.16
WER 44.5% 54.5%
STD performance on the RT04S task
Spoken term detection
termtrue
termspuriouscorrect
termN
termNtermN
)(
)}(1.0)({
Actual term-weighted value(ATWV)
)}()({1 termPtermP FAMiss
term
average
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.
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.
Current experimental results
Language identification
parallel phone/grapheme recognizer architecture for LID
Current experimental results
DER%
phone grapheme Phone+grapheme
unit likelihood 35.6 32.1 27.9
sentence likelihood 46.8 39.6 39.4
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.
Work plan
Phase I: Simple grapheme-based system1. 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 modeling1. 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 ISPM1. Finish the test on the STD task (by May 2009).2. Finish the test on the LID task (by May 2009).