Get Robust Speech Recognition of Uncertain or Missing Data: PDF
By Dorothea Kolossa, Reinhold Haeb-Umbach
Automatic speech acceptance suffers from a scarcity of robustness with appreciate to noise, reverberation and interfering speech. The transforming into box of speech attractiveness within the presence of lacking or doubtful enter facts seeks to ameliorate these difficulties by utilizing not just a preprocessed speech sign but in addition an estimate of its reliability to selectively specialize in these segments and contours which are top-quality for reputation. This publication provides the state-of-the-art in popularity within the presence of uncertainty, delivering examples that make the most of uncertainty info for noise robustness, reverberation robustness, simultaneous popularity of a number of speech indications, and audiovisual speech recognition.
The publication is acceptable for scientists and researchers within the box of speech reputation who will locate an summary of the state-of-the-art in strong speech acceptance, pros operating in speech acceptance who will locate techniques for making improvements to attractiveness ends up in a number of stipulations of mismatch, and academics of complex classes on speech processing or speech reputation who will discover a reference and a complete creation to the sector. The ebook assumes an knowing of the basics of speech reputation utilizing Hidden Markov Models.
Read or Download Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications PDF
Best intelligence & semantics books
Offers a set of comparable functions and a theoretical improvement of a normal structures thought. starts off with ancient heritage, the fundamental good points of Cantor's naive set conception, and an advent to axiomatic set thought. the writer then applies the idea that of centralizable structures to sociology, makes use of the trendy platforms concept to retrace the historical past of philosophical difficulties, and generalizes Bellman's precept of optimality.
Bayesian nets are regular in synthetic intelligence as a calculus for informal reasoning, allowing machines to make predictions, practice diagnoses, take judgements or even to find informal relationships. yet many philosophers have criticized and eventually rejected the valuable assumption on which such paintings is based-the causal Markov situation.
A complete advisor to studying applied sciences that unencumber the price in vast information Cognitive Computing presents precise assistance towards construction a brand new category of platforms that research from adventure and derive insights to free up the price of huge information. This e-book is helping technologists comprehend cognitive computing's underlying applied sciences, from wisdom illustration thoughts and traditional language processing algorithms to dynamic studying methods in accordance with accrued facts, instead of reprogramming.
- The lambda calculus: its syntax and semantics
- Practical Applications of Evolutionary Computation to Financial Engineering: Robust Techniques for Forecasting, Trading and Hedging
- Knowledge-Based Software Engineering: Proceedings of the Fifth Joint Conference on Knowledge-Based Software Engineering
- Artificial neural networks and statistical pattern recognition : old and new connections
- Language processing in social context
Additional resources for Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications
In: Proc. of Annual Conference of the International Speech Communication Association (Interspeech). Makuhari, Japan (2010) 12. : Speech feature estimation under the presence of noise with a switching linear dynamical model. In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toulouse, France (2006) 13. : Noisy speech feature estimation on the Aurora2 database using a switching linear dynamic model. Journal of Multimedia 2(2), 47–52 (2007) 14. : Large vocabulary speech recognition under adverse acoustic environments.
Of Annual Conference of the International Speech Communication Association (Interspeech). Lisbon, Portugal (2005) 37. : Issues with uncertainty decoding for noise robust speech recognition. Speech Commununication 50, 265–277 (2008) 38. , Tan, Z. ): Automatic Speech Recognition on Mobile Devices and over Communication Networks. Springer, London (2008) 39. : From missing data to maybe useful data: Soft data modelling for noise robust ASR. Proc. WISP 06 (2001) 40. : Probabilistic optimum filtering for robust speech recognition.
192–195. Denver, Co. (2002) 16. : Enhancement of log Mel power spectra of speech using a phase-sensitive model of the acoustic environment and sequential estimation of the corrupting noise. IEEE Transactions on Speech and Audio Processing 12(2), 133 – 143 (2004) 17. : Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion. IEEE Trans. Speech and Audio Processing 13(3), 412–421 (2005) 18. : Noise robust speech recognition with a switching linear dynamic model.
Robust Speech Recognition of Uncertain or Missing Data: Theory and Applications by Dorothea Kolossa, Reinhold Haeb-Umbach