Cross-words reference template for dtw-based speech recognition systems

Abdulla, Waleed H.; Chow, David; Sin, Gang Ho

doi:10.1109/tencon.2003.1273186

Cited by 79 publications

(89 citation statements)

References 7 publications

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“…Both ARs obtained by using one template per gesture (Min-I and RD-1) are worse than ARs using four templates per gesture (Min-4 and RD-4). This shows that the AR of DTW recognizer can be improved by increasing the number of class templates per class, but the trade-off is that the computational time is raised from an average of 17 The local minimum deviations (LMDs) of all 12 gestures from Min-4, RD-4, Min-l, and RD-1 are shown in Table 2. Most of the LMDs are less than 7%, except two special cases.…”

Section: Case Studies and Results A Experimental Setup And Evaluationmentioning

confidence: 99%

Online Context Recognition in Multisensor Systems using Dynamic Time Warping

West

Venkatesh

et al. 2005

2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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In this paper we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic lime Warping (D7W) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performning two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy.

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Section: Case Studies and Results A Experimental Setup And Evaluationmentioning

confidence: 99%

Online Context Recognition in Multisensor Systems using Dynamic Time Warping

West

Venkatesh

et al. 2005

2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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“…For those who use k-means clustering, Euclidean distance metric is often used for time series average. This is also true in other domains such as speech recognition and pattern recognition [1][6] [9] [14], which perhaps is a good indicator flagging problems in DTW averaging method. Despite many proposed shape averaging algorithms, most of them provide method for specific domains [3][11] [12], such as evoked potential in medical domains.…”

Section: Introductionmentioning

confidence: 91%

Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data

Niennattrakul

Ratanamahatana

2007

Computational Science – ICCS 2007

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Abstract.Shape averaging or signal averaging of time series data is one of the prevalent subroutines in data mining tasks, where Dynamic Time Warping distance measure (DTW) is known to work exceptionally well with these time series data, and has long been demonstrated in various data mining tasks involving shape similarity among various domains. Therefore, DTW has been used to find the average shape of two time series according to the optimal mapping between them. Several methods have been proposed, some of which require the number of time series being averaged to be a power of two. In this work, we will demonstrate that these proposed methods cannot produce the real average of the time series. We conclude with a suggestion of a method to potentially find the shape-based time series average.

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“…A direct manner to estimate the centroid is proposed in Abdulla et al [1], where a dynamic time warping between each time series and a reference one, generally the time series medoid, is first performed. Each time series is then described in the representation space defined by the reference medoid by resampling, stretching and shortening operations, as in Figure 3.…”

Section: Cross-word Reference Template (Cwrt)mentioning

confidence: 99%

“…Estimating the centroid of a set of time series under time warp however faces the tricky multiple temporal alignment problem [1][2][3][4]. Temporal warping alignment of time series has been an active research topic in many scientific disciplines.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp

Soheily-Khah¹,

Douzal-Chouakria²,

Gaussier³

2016

Lecture Notes in Computer Science

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Abstract. Estimating the centroid of a set of time series under time warp is a major topic for many temporal data mining applications, as summarization a set of time series, prototype extraction or clustering. The task is challenging as the estimation of centroid of time series faces the problem of multiple temporal alignments. This work compares the major progressive and iterative centroid estimation methods, under the dynamic time warping, which currently is the most relevant similarity measure in this context.

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Cross-words reference template for dtw-based speech recognition systems

Cited by 79 publications

References 7 publications

Online Context Recognition in Multisensor Systems using Dynamic Time Warping

Online Context Recognition in Multisensor Systems using Dynamic Time Warping

Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data

A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp

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