1. Dealing with Unknown Unknowns (in Speech Recognition) Hynek Hermansky Processing speech in multiple parallel processing streams, which attend to different parts of signal space and use different strengths of prior top-down knowledge is proposed for dealing with unexpected signal distortions and with unexpected lexical items. Some preliminary results in machine recognition of speech are presented.

2. indian white man There are things we do not know we don't know. Donald Rumsfeld

3. “Funding artificial intelligence is real stupidity” "After growing wildly for years, the field of computing appears to be reaching its infancy.” Research field of “mad inventors or untrustworthy engineers” supervised the Bell Labs team which built the first transistor President’s Science Advisory Committee developed the concept of pulse code modulation designed and launched the first active communications satellite Letter to Editor J.Acoust.Soc.Am..... should people continue work towards speech recognition by machine ? Perhaps it is for people in the field to decide.

4. Why am I working in this field? Problems faced in machine recognition of speech reveal basic limitations of all information technology ! Why did I climbed Mt. Everest? Because it is there ! -Sir Edmund Hilary Spoken language is one of the most amazing accomplishments of human race. access to information voice interactions with machines extracting information from speech data !

5. production, perception, cognition,.. knowledge We speak in order to hear, in order to be understood. -Roman Jakobson data Speech recognition …a problem of maximum likelihood decoding -Frederick Jelinek Hidden Markov Model

6. Ŵ = argmax W p(x|W) P(W) Ŵ – estimated speech utterance p(x|W i ) - likelihoods of acoustic models of speech sounds, the models are derived by training on very large amounts of speech data P(W) - prior probabilities of speech utterances (language model), model estimated from large amounts of data (typically text) Stochastic recognition of speech “Unknown unknowns” in machine recognition of speech distortions not seen in the training data of the acoustic model words that are not expected by the language model

7. One possible way of dealing with unknown unknowns Parallel information-providing streams, each carrying different redundant dimensions of a given target. A strategy for comparing the streams. A strategy for selecting “reliable” streams. Stream formation Different perceptual modalities Different processing channels within each modality Bottom-up and top-down dominated channels signal information fusion decision Comparing the streams ? various correlation (distance) measures Selecting reliable streams ????? Information in speech is coded in many redundant dimensions. Not all dimensions get corrupted at the same time.

8. Fletcher et al Probability of error of recognition of full-band speech is given by a product of probabilities of errors in subbands Boothroyd and Nittrouer Probability of error of recognition in contexts is given by a product of probabilities of errors of recognition without context and probability of error in channel which provides information about the context Final error dominated by the channel with smallest error ! Perceptual Data

9. A large number of parallel processing streams Different carrier frequencies Different carrier bandwidths Different spectral and temporal resolutions Different modalities Different prior biases Processing streams different carrier frequencies different temporal resolutions different spectral resolutions Auditory cortical receptive fields Evidence for different processing strategies time [s] frequency from N. Mesgarani

10. Evidence for equally powerful bottom-up and top-down streams ? From the subjective point of view, there is nothing special that would differentiate between the top-down and bottom-up dominated processing streams. All streams provide information for a decision. When all streams provide non-conflicting information, all this information is used for the decision. When the context allows for multiple interpretations of the sensory input, the bottom-up processing stream dominates. When the sensory input gets corrupted by noise, the top-down dominated stream fills in for the corrupted bottom-up input. Hermansky 2013

11. Monitoring Performance P1P1 P2P2 P miss = (1-P 1 )(1-P 2 ) Could it be that we know when we know ? observer - false positives and negatives are possible P miss_observed ≠ (1-P 1 )(1-P 2 )

12. Knowing when one knows ! Performance Monitoring in Sensory Perception picture density low high judgement 0 % 100 % sparse dense not sure human judgment (adopted from Smith et al 2003) similar data available for monkeys, dolphins, rats,… update classifier model of the output testing data training data classifier compare models model of the output Machine ?

13. time frequency data preprocessing artificial neural network trained on large amounts of labeled data Spectrogram Posteriogram ANN fusion phoneme posteriors up to 1 s

14. Fusion of streams of different carrier frequencies [Hermansky et al 1996, Li et al 2013]

15. Preliminary results using multi-stream speech recognition on noisy TIMIT data Processing is done in multiple parallel streams Signal corruption affects only some streams Performance monitor selects N best streams for further processing environmentconventionalproposedbest by hand clean31 %28 %25 % car at 0 dB SNR54 %38 %35 % Phoneme recognition error rates on noisy TIMIT data

16. up to 1000 ms high frequency components many processing layers (transformed) posterior probabilities of speech sounds mid frequency components low frequency components “smart” fusion up to 100 ms all available frequency components many processing layers (transformed) posterior probabilities of speech sounds conventional “deep” net “long, wide and deep”net time get info 1 get info i get info N

17. Conclusions we would eventually like to make Recognition should be done in parallel processing streams, each attending to a particular aspect of the signal and using different levels of top-down expectations Discrepancy among the streams indicates an unexpected signal Suppressing corrupted streams can increase robustness to unexpected inputs

18. Machine Emulation of Human Speech Communication..devise a clear, simple, definitive experiments. So a science of speech can grow, certain step by certain step. John Pierce human communication, speech production, perception, neuroscience, cognitive science,.. We speak, in order to be heard, in order to be understood Roman Jakobson Speech recognition …a problem of maximum likelihood decoding information and communication theory, machine learning, large data,…. Fred Jelinek The complexity for minimum component costs has increased at a rate of roughly a factor of two per year… Gordon Moore tools also John Pierce: ( Speech recognition is so far (1969) field of) mad inventors or untrustworthy engineers (because machine needs) intelligence and knowledge of language comparable to those of a native speaker. Sounds like a good goal to aim at !

19. Nima Mesgarani Samuel Thomas Feipeng Li Ehsan Variani Vijay Peddinti THANKS ! Jont Allen Harish Mallidi Misha PavelHamed Ketabdar