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Frederico Rodrigues and Isabel Trancoso

Robust Recognition of Digits and Natural Numbers. Frederico Rodrigues and Isabel Trancoso. INESC/IST, 2000. Summary. Problem overview Baseline system Extensions to the baseline system Conclusions and future work. Microphone. Microphone. Position. Channel. Distortion. Distortion.

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Frederico Rodrigues and Isabel Trancoso

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  1. Robust Recognition of Digits and Natural Numbers Frederico Rodrigues and Isabel Trancoso INESC/IST, 2000

  2. Summary • Problem overview • Baseline system • Extensions to the baseline system • Conclusions and future work

  3. Microphone Microphone Position Channel Distortion Distortion Speaker Noise Gender Age Vocal tract characteristics Pronunciation Rate of Speech Stress Lombard Reflex Environment Background noises Intermitent noises Coktail party noises Reverberation The Problem

  4. Corpus Description • Multilingual telephone speech corpus • SPEECHDAT(M) 1000 speakers • SPEECHDAT(II) 4000 speakers • Orthographically transcribed including noise events

  5. Noise events • [spk] : Speaker related noises • [sta] : Stationary noises • [int] : Intermittent noises

  6. Train and Test Set Definition • Selection procedure • Age, gender and region distribution are approximately equal in both train and test sets; • SPEECHDAT II • Fixed 500 speakers evaluation set • Additional 300 speakers development set • SPEECHDAT(M) • 200 speakers evaluation set • Overall ratio of 80% Train/20% Test

  7. Sub-corpus Used • I1 - Isolated digit strings • B1 - Sequences of 10 digits • N* - Natural numbers

  8. Feature Extraction • MFCC (Mel Frequency Cepstral Coefficients) • 14 Cepstra + 14  Cepstra + Energy +  Energy • Speech signal band-limited between 200 and 3800 Hz • Hamming Window: 25 ms each 10 ms • Cepstral Mean Substraction • Simple but effective technique for channel and speaker normalization

  9. Acoustic Modeling • Left-right continuous density HMM’s • Word models for each digit. No skips. • Silence and filler models with forward and backward skips • Gender dependent models HMM: Hidden Markov Model

  10. Model Topology Fillers and silence models topology

  11. Baseline System - Isolated Digits • Choose isolated digits with no noise marks • HMM parameters initialized with the global mean and variance of the training data • Embedded Baum-Welch Reestimation • Evaluate performance withViterbi decoding • Grammar allowing one digit and initial and final silence • Grammar allowing one digit and any number of fillers or silence

  12. Baseline System - Isolated Digits

  13. Baseline System - Isolated Digits • Increment Gaussian mixtures per state up to 3 for the digit models • Introduce files with noise marks • Repeat re-estimation/evaluation process • Increment Gaussian mixtures per state up to 3 for the filler and digit models

  14. Connected vs Isolated Digits Example: Number 3 1 2 6 said as: Isolated Digits: t r e S u~ d o j S s 6 j S Connected Digits: t r e z u~ d o j S _ 6 j S

  15. Baseline System - Connected Digits • Use best isolated digit models as bootstrap models • Repeat re-estimation/evaluation process • Increment gradually Gaussian mixtures per state up to 5 for the digit models

  16. Baseline System - Results

  17. Extension to the Baseline System • New way of modelling the filler models • Same training/evaluation process • Train the 9 filler and silence models with no skips • Build a unique filler model concatenating all filler and silence models

  18. New Filler Model Arquitecture

  19. Results With New Filler Model

  20. Natural Numbers • Phone models with 3 states and no skips • Larger vocabulary size • May be adapted to other tasks • Phones initialized from models already trained for a directory assistance task • Digits are still modeled by word models • Grammar for natural numbers ranging from zero to hundreds of millions

  21. Natural Numbers Example Number 25: Hypothesis 1: vinte e cinco (Twenty and five) Hypotesis 2: vinte cinco (Twenty five) But “vinte cinco” could also be the sequence of natural numbers: 20 5

  22. Natural Numbers - Results

  23. Feature Extraction User State Control Speech Recognition Speech Synthesis Speech Recording DIXI - SVIT Speech Prompts Answer Speech / Commands Synthesised answer/ Commands Client Server Sample Application

  24. Conclusions and Future Work • Explicitly modeling fillers is a difficult task • Improved filler model decreases error rate up to 50 % • Develop context dependent models • Solve vowel reduction and co-articulation problems • Results may be improved through the use of discriminative training techniques

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