1. ON REAL-TIME MEAN-AND-VARIANCE NORMALIZATION OF SPEECH RECOGNITION FEATURES Pere Pujol, Dušan Macho, and Climent NadeuNational ICT TALP Research Center Universitat Politècnica de Catalunya, Barcelona, Spain. Presenter: Chen, Hung-Bin ICASSP 2006

2. Outline Introduction On-line versions of the mean and variance normalization(MVN) Experiments Conclusions

3. Introduction On real-time system, However, the off-line estimation (MVN) involves a long delay that is likely unacceptable In this paper, we report an empirical investigation about several on-line versions of the mean and variance normalization(MVN) technique and the factors affecting their performance –Segment-based updating of mean & variance –Recursive updating of mean & variance

4. INVOLVED IN REAL-TIME MVN Mean and variance normalization

5. on-line versions issues Segment-based updating of mean & variance –there is a delay of half the length of the window Recursive updating of mean & variance –initialized using the first D frames of the current utterance and then they are recursively updated as new frames arrive n-D/2nn+D/2 D 0 D

6. EXPERIMENTAL SETUP AND BASELINE RESULTS A subset of the office environment recordings from the Spanish version of the Speecon database –carry out the speech recognition experiments with digit strings The database includes recordings with 4 microphones: –a head-mounted close-talk (CT) –a Lavalier mic –a directional mic situated at 1 meter from the speaker –an omni-directional microphone placed at 2-3 meters from the speaker 125 speakers were chosen for training and 75 for testing –both balanced in terms of sex and dialect

7. baseline system results the resulting parameters were used as features, along with their first- and second-order time derivatives

8. Segment-based updating of mean & variance results a sliding fixed-length window centered in the current frame –there is a delay of half the length of the window n-D/2nn+D/2 D

9. Recursive updating of mean & variance results Table 3 shows the results using different look-ahead values in the recursive MVN –The entire look-ahead interval is used to calculate the initial estimates of mean and varianc –in the case of 0 sec, the first 100 ms are used 0 D

10. Recursive updating of mean & variance results The initial estimates were computed as in UTT-MVN This reinforces the good initial estimates of mean & variance –In our case β was experimentally set to 0.992

11. INITIAL MEAN & VARIANCE ESTIMATED FROM PAST DATA ONLY this category do not use the current utterance to compute the initial estimates of the mean & variance of the features the different data sources –Current session: in the current session with fixed microphones, environment and speaker utterances not included in the test set –Set of sessions: using utterances from a set of sessions of the Speecon database instead of utterances from the same session

12. initial with past data results

13. Conclusions In that case, a recursive MVN performs better than a segment-based MVN we observed that –the usefulness of mean and variance updating increases when the initial estimates are not representative enough for a given utterance

14. GENERALIZED PERCEPTUAL FEATURES FOR VOCALIZATION ANALYSIS ACROSS MULTIPLE SPECIES Patrick J. Clemins, Marek B. Trawicki, Kuntoro Adi, Jidong Tao, and Michael T. Johnson Marquette University Department of Electrical and Computer Engineering Speech and Signal Processing Lab Presenter: Chen, Hung-Bin ICASSP 2006

15. 15 Outline Introduction The greenwood function cepstral coefficient (gfcc) model Experiments Conclusions

16. 16 Introduction This paper introduces the Greenwood Function Cepstral Coefficient (GFCC) and Generalized Perceptual Linear Prediction (GPLP) feature extraction models for the analysis of animal vocalizations across arbitrary species Mel-Frequency Cepstral Coefficients (MFCCs) and Perceptual Linear Predication (PLP) coefficients are well-established feature representations for human speech analysis and recognition tasks –W e want to generalize the frequency warping component across arbitrary species, we build on the work of Greenwood

17. 17 Greenwood function cepstral coefficient (gfcc) model Greenwood found that many mammals perceived frequency on a logarithmic scale along the cochlea –He modeled this relationship with an equation of the form where a, A, and k are species-specific constants and x is the cochlea position –This equation can be used to define a frequency warping through the following equations for real frequency, f, and perceived frequency, fp

18. 18 Greenwood function cepstral coefficient (gfcc) model Assuming this value for k, the other two constants can be solved for given the approximate hearing range (fmin – fmax) of the species under study –By setting Fp(fmin) = 0 and Fp (fmax) = 1, the following equations for a and A are derived –Thus, using the species specific values for fmin and fmax and an assumed value of k=0.88, a frequency warping function can be constructed

19. 19 Greenwood function cepstral coefficient (gfcc) model Figure 1 shows GFCC filter positioning for African Elephant and Passeriformes (songbird) species compared to the Mel- Frequency scale

20. 20 Greenwood function cepstral coefficient (gfcc) model Figure 2 shows GPLP equal loudness curves for African Elephant and Passeriformes (songbird) species compared to the human equal loudness curve

21. 21 experiments Speaker independent song-type classification experiments were performed across the 5 most common song types using 50 exemplars of each song-type, each containing multiple song-type variants.

22. 22 experiments Song-type dependent speaker identification experiments were performed using 25 exemplars of the most frequent song-type for each of the 6 vocalizing buntings.

23. 23 experiments Call-type dependent speaker identification experiments were performed using the entire Loxodonta Africana dataset –There were a total of six elephants with 20, 30, 14, 17, 34, and 28 rumble exemplars per elephant

24. 24 Conclusions New feature extraction models have been introduced for application to analysis and classification tasks of animal vocalizations.