NDLI: Minimum mean squared error time series classification using an echo state network prediction model

Content Provider	IEEE Xplore Digital Library
Author	Skowronski, M.D. Harris, J.G.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL (Skowronski, M.D.; Harris, J.G.)
Abstract	The echo state network (ESN) has been recently proposed as an alternative recurrent neural network model. An ESN consists of a reservoir of conventional processing elements, which are recurrently interconnected with untrained random weights, and a readout layer, which is trained using linear regression methods. The key advantage of the ESN is the ability to model systems without the need to train the recurrent weights. In this paper, we use an ESN to model the production of speech signals in a classification experiment using isolated utterances of the English digits "zero" through "nine." One prediction model for each digit was trained using frame-based speech features (cepstral coefficients) from all train utterances, and the readout layer consisted of several linear regressors which were trained to target different portions of the time series using a dynamic programming algorithm (Viterbi). Each novel test utterance was classified with the label from the digit model with the minimum mean squared prediction error. Using a corpus of 4130 isolated digits from 8 male and 8 female speakers, the highest classification accuracy attained with an ESN was 100.0% (99.1%) on the train (test) set, compared to 100% (94.7%) for a hidden Markov model (HMM). HMM performance increased to 100.0% (99.8%) when context features (first- and second-order temporal derivatives) were appended to the cepstral coefficients. The ESN offers an attractive alternative to the HMM because of the ESN's simple train procedure, low computational requirements, and inherent ability to model the dynamics of the signal under study
Sponsorship	The Inst. of Electr. and Electron. Eng., Inc. Circuits and Syst. Soc. (IEEE CASS)
Starting Page	4
Ending Page	3156
File Size	1463419
Page Count	3153
File Format	PDF
ISBN	0780393899
DOI	10.1109/ISCAS.2006.1693294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-05-21
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Hidden Markov models Speech Cepstral analysis Testing Recurrent neural networks Reservoirs Linear regression Dynamic programming Heuristic algorithms
Content Type	Text
Resource Type	Article

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