A Linked-HMM for Robust Voicing and Speech Detection

1. A Linked-HMM for Robust Voicing and Speech Detection Presented by: Emiliano Miluzzo

2. why the mic is important as a sensor for a people-centric sensing approach?

3. In few words… • Linked-HMM for simultaneous and robust voicing and speech detection

4. In few words… • Linked-HMM for simultaneous and robust voicing and speech detection • Targeting different experimental settings: low-sampling rates, far-field mic, ambient noise.

5. In few words… • Linked-HMM for simultaneous and robust voicing and speech detection • Targeting different experimental settings: low-sampling rates, far-field mic, ambient noise. • Features independent of energy.

6. In few words… • Linked-HMM for simultaneous and robust voicing and speech detection • Targeting different experimental settings: low-sampling rates, far-field mic, ambient noise. • Features independent of energy. • Exploit speech patterns, usually combinations of talking and silence segments.

8. What’s nice about the paper • The first paper presenting the application of linked-HMM for speech and voice detection.

9. What’s nice about the paper • The first paper presenting the application of linked-HMM for speech and voice detection. • “simple” algorithm: forward-backward algorithm, features extraction

10. What’s nice about the paper • The first paper presenting the application of linked-HMM for speech and voice detection. • “simple” algorithm: forward-backward algorithm, features extraction. • Experimental evaluation of some of the aspects of the proposed algorithms.

11. What’s nice about the paper • The first paper presenting the application of linked-HMM for speech and voice detection. • “simple” algorithm: forward-backward algorithm, features extraction. • Experimental evaluation of some of the aspects of the proposed algorithms. • I learned something useful, namely how to get rid of the impact of constant source contribution (fan, wind blowing, etc.). 

12. How about the cons? • Fairly dense of concepts for a short paper.

13. How about the cons? • Fairly dense of concepts for a short paper. • Consequently, often lack of clear explanations.

14. How about the cons? • Fairly dense of concepts for a short paper. • Consequently, often lack of clear explanations. • Generally applicable, to mobile devices such as cell phones for example?

15. How about the cons? • Fairly dense of concepts for a short paper. • Consequently, often lack of clear explanations. • Generally applicable, to mobile devices such as cell phones for example? • Training with too few different individuals (just 2) – this is a supervised ML method!!

16. How about the cons? • Fairly dense of concepts for a short paper. • Consequently, often lack of clear explanations. • Generally applicable, to mobile devices such as cell phones for example? • Training with too few different individuals (just 2) – this is a supervised ML method!! • Not clear experimental protocol – what does “noisy conditions” mean?? • Is comparison in Fig. 3 enough to show the improvement over HMM?

17. Is the noise autocorrelation always effective? • What if the noise is generated by a high energy periodic noisy signal such as a motor?

18. Is the noise autocorrelation always effective? • What if the noise is generated by a high energy periodic noisy signal such as a motor? • This suggests that the proposed technique might …..

19. Is the noise autocorrelation always effective? • What if the noise is generated by a high energy periodic noisy signal such as a motor? • This suggests that the proposed technique might work better in indoor environment whereas performs more poorly on mobile devices?

20. Is the noise autocorrelation always effective? • What if the noise is generated by a high energy periodic noisy signal such as a motor? • This suggests that the proposed technique might work better in indoor environment whereas performs more poorly on mobile devices? • Not clear how variations of one of the features (particularly, noisy autocorrelation) would impact the overall classification result.

21. Few questions • How does the algorithm differentiate a singer singing a song from an actual conversation?

22. Few questions • How does the algorithm differentiate a singer singing a song from an actual conversation? • Maybe checking if the spectral content of the voicing part changes over time is an indication of multiple people talking

23. Few questions • How does the algorithm differentiate a singer singing a song from an actual conversation? • Maybe checking if the spectral content of the voicing part changes over time is an indication of multiple people talking • Does the system distinguish conversations from a pair of speakers A versus the pair of speakers B?

24. Few questions • How does the algorithm differentiate a singer singing a song from an actual conversation? • Maybe checking if the spectral content of the voicing part changes over time is an indication of multiple people talking • Does the system distinguish conversations from a pair of speakers A versus the pair of speakers B? • Same as above plus knowledge of the device owner voice spectral pattern would help to filter out outliers

25. Overall • Nice technique that could be applied to a broad set of scenarios, in my opinion mainly where computational resources are available and not many sources of (periodic) noise are present. In these cases the error is small.

26. Overall • Nice technique that could be applied to a broad set of scenarios, in my opinion mainly where computational resources are available and not many sources of (periodic) noise are present. In these cases the error is small. • Not sure about its applicability to mobile devices for real-time speech detection. Some of the aspects might be re-used though.

27. Overall • Nice technique that could be applied to a broad set of scenarios, in my opinion mainly where computational resources are available and not many sources of (periodic) noise are present. In these cases the error is small. • Not sure about its applicability to mobile devices for real-time speech detection. Some of the aspects might be re-used though. • Can a mobile-devices oriented scheme tradeoff accuracy versus speed?