Compact Representation of Speech Using 2-D Cepstrum – An Application to Slovak Digits Recognition

Jarina, Roman; Kuba, Michal; Paralic, Martin

doi:10.1007/11551874_44

Cited by 5 publications

(1 citation statement)

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“…The HMM-based phone recognizer uses Mel-frequency cepstral coefficient 12 (MFCC) and the RF-based onset detector uses two-dimensional cepstral coefficient (TDCC). [13][14][15] The second part of the section introduces two statistical learning machines applied to the method. The first one is an HMMbased forced alignment technique, and the second one is a classification technique called a random forest.…”

Section: Acoustic Features and Statistical Learning Machinesmentioning

confidence: 99%

Automatic estimation of voice onset time for word-initial stops by applying random forest to onset detection

Lin

Wang

2011

The Journal of the Acoustical Society of America

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The voice onset time (VOT) of a stop consonant is the interval between its burst onset and voicing onset. Among a variety of research topics on VOT, one that has been studied for years is how VOTs are efficiently measured. Manual annotation is a feasible way, but it becomes a time-consuming task when the corpus size is large. This paper proposes an automatic VOT estimation method based on an onset detection algorithm. At first, a forced alignment is applied to identify the locations of stop consonants. Then a random forest based onset detector searches each stop segment for its burst and voicing onsets to estimate a VOT. The proposed onset detection can detect the onsets in an efficient and accurate manner with only a small amount of training data. The evaluation data extracted from the TIMIT corpus were 2344 words with a word-initial stop. The experimental results showed that 83.4% of the estimations deviate less than 10 ms from their manually labeled values, and 96.5% of the estimations deviate by less than 20 ms. Some factors that influence the proposed estimation method, such as place of articulation, voicing of a stop consonant, and quality of succeeding vowel, were also investigated.

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