Integration of acoustic and articulatory information with application to speech recognition

Leung, Kim Hung; Siu, Man-Hung

doi:10.1016/j.inffus.2003.10.007

Cited by 3 publications

(1 citation statement)

References 20 publications

(25 reference statements)

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“…Perhaps the most interesting direction of further research could be the use of articulatory (phonological) features based on theories of phonology (Momayyez et al 2007;Leung and Siu 2004;Livescu et al 2003;Ogata and Ariki 2003;Ahadi 2000). Articulatory features involve use of phonological feature detectors such as neural networks that provide the features as output for further modeling.…”

Section: Discussionmentioning

confidence: 99%

Dual stream speech recognition using articulatory syllable models

Puurula

Compernolle

2010

Int J Speech Technol

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Recent theoretical developments in neuroscience suggest that sublexical speech processing occurs via two parallel processing pathways. According to this Dual Stream Model of Speech Processing speech is processed both as sequences of speech sounds and articulations. We attempt to revise the "beads-on-a-string" paradigm of Hidden Markov Models in Automatic Speech Recognition (ASR) by implementing a system for dual stream speech recognition. A baseline recognition system is enhanced by modeling of articulations as sequences of syllables. An efficient and complementary model to HMMs is developed by formulating Dynamic Time Warping (DTW) as a probabilistic model. The DTW Model (DTWM) is improved by enriching syllable templates with constrained covariance matrices, data imputation, clustering and mixture modeling. The resulting dual stream system is evaluated on the N-Best Southern Dutch Broadcast News benchmark. Promising results are obtained for DTWM classification and ASR tests. We provide a discussion on the remaining problems in implementing dual stream speech recognition.

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Section: Discussionmentioning

confidence: 99%

Dual stream speech recognition using articulatory syllable models

Puurula

Compernolle

2010

Int J Speech Technol

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Combining formant frequency based on variable order LPC coding with acoustic features for TIMIT phone recognition

Messaoud

Hamida

2011

Int J Speech Technol

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Combination of multiple acoustic features has great potential to improve Automatic Speech Recognition (ASR) accuracy. Our contribution in this research was to investigate one novel method when using voiced formants' features in combination with standard MFCC features in order to enhance TIMIT phone recognition. These voiced features provide accurate formants frequencies using a Variable Order LPC Coding (VO-LPC) algorithm that was combined with continuity constraints. The overall estimating formants were concatenated with MFCC features when a voiced frame could be detected. For feature-level combination, Heteroscedastic Linear Discriminant Analysis (HLDA) based approach had been used successfully to find an optimal linear combination of successive vectors of a single feature stream.A series of experiments on phone recognition speakerindependent continuous-speech had been carried out using a subset of the large read-speech TIMIT phone corpus. Hidden Markov Model Toolkit (HTK) was also used throughout all carried experiments. Using such feature level combination, optimized mixture splitting and a bigram language model, a detailed analysis on this combination performance was discussed for Context-Independent (CI) and ContextDependent (CD) Hidden Markov Models (HMM). Experimental results from our proposed procedure showed that phone error rate was successfully decreased by about 3%. At phonetic level group, an increase of 8% and of 10% was observed respectively for vowel and liquid group. These results proved clear phone enhancement regarding existing state of the art.

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Articulatory-feature-based confidence measures

Leung¹,

Siu²

2006

Computer Speech & Language

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Integration of acoustic and articulatory information with application to speech recognition

Cited by 3 publications

References 20 publications

Dual stream speech recognition using articulatory syllable models

Dual stream speech recognition using articulatory syllable models

Combining formant frequency based on variable order LPC coding with acoustic features for TIMIT phone recognition

Articulatory-feature-based confidence measures

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