Combining evidence from multiple classifiers for recognition of consonant-vowel units of speech in multiple languages

Gangashetty, Suryakanth V.; Sekhar, C. Chandra; Yegnanarayana, B.

doi:10.1109/icisip.2005.1529482

Cited by 9 publications

(27 citation statements)

References 2 publications

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“…In [15] they acknowledge the fact that the classification error patterns from SVM and HMM classifiers may be different and thus their combination could result in a gain in performance. They assess this statement on a classification task of consonant-vowel units of speech in several Indian languages obtaining a marginal gain by using a sum rule combination scheme of the two classifiers' evidences.…”

Section: Non-uniform Feature Sequence Resamplingmentioning

confidence: 99%

Robust ASR using Support Vector Machines

Solera-Ureña

Martín-Iglesias

Gallardo-Antolín

et al. 2007

Speech Communication

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The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units.In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM-SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in [1] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.

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Section: Non-uniform Feature Sequence Resamplingmentioning

confidence: 99%

Robust ASR using Support Vector Machines

Solera-Ureña

Martín-Iglesias

Gallardo-Antolín

et al. 2007

Speech Communication

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“…This problem has been overcome by state clustering in models [11]. Recent studies have shown that syllables (combinations of phones) are also promising subword units for speech recognition [3,4,8,19,21]. Context-dependent units such as syllables capture significant co-articulation effects and pronunciation variations compared to phones.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the syllable-like units are of type C m V C n , where C refers to a consonant, V refers to a vowel, and m and n refer to the number of consonants preceding and following the vowel, respectively. Among these units, the consonant vowel (CV) units are the most frequently (around 90% in Indian languages) occurring units [4]. Hence CV units are considered for our studies in this work.…”

Section: Introductionmentioning

confidence: 99%

Spotting and Recognition of Consonant-Vowel Units from Continuous Speech Using Accurate Detection of Vowel Onset Points

Vuppala

Rao

Chakrabarti

2012

Circuits Syst Signal Process

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In this paper, we propose an efficient approach to spotting and recognition of consonant-vowel (CV) units from continuous speech using accurate detection of vowel onset points (VOPs). Existing methods for VOP detection suffer from lack of high accuracy, spurious VOPs, and missed VOPs. The proposed VOP detection is designed to overcome most of the shortcomings of the existing methods and provide accurate detection of VOPs for improving the performance of spotting and recognition of CV units. The proposed method for VOP detection is carried out in two levels. At the first level, VOPs are detected by combining the complementary evidence from excitation source, spectral peaks, and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in the vowel regions. The spotted CV units are recognized using a two-stage CV recognizer. Two-stage CV recognition system consists of hidden Markov models (HMMs) at the first stage for recognizing the vowel category of a CV unit and support vector machines (SVMs) for recognizing the consonant category of a CV unit at the second stage. Performance of spotting and recognition of CV units from continuous speech is evaluated using Telugu broadcast news speech corpus.

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“…Some of them perform a previous processing of the speech or feature sequence in order to obtain fixed dimension vectors that fit the SVM input. This normalization can be achieved by means of simple uniform [35] or non-uniform [36], [37] feature sequence resampling procedures. Other authors apply the socalled triphone model approach, which assumes that speech segments corresponding to phones are composed of a fixed number of sections (3 in most cases).…”

Section: Hybrid Svm/hmm Systemsmentioning

confidence: 99%

Real-Time Robust Automatic Speech Recognition Using Compact Support Vector Machines

Solera-Ureña

García-Moral²,

Peláez-Moreno

et al. 2012

IEEE Trans. Audio Speech Lang. Process.

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Abstract-In the last years, support vector machines (SVMs) have shown excellent performance in many applications, especially in the presence of noise. In particular, SVMs offer several advantages over artificial neural networks (ANNs) that have attracted the attention of the speech processing community. Nevertheless, their high computational requirements prevent them from being used in practice in automatic speech recognition (ASR), where ANNs have proven to be successful. The high complexity of SVMs in this context arises from the use of huge speech training databases with millions of samples and highly overlapped classes. This paper suggests the use of a weighted least squares (WLS) training procedure that facilitates the possibility of imposing a compact semiparametric model on the SVM, which results in a dramatic complexity reduction. Such a complexity reduction with respect to conventional SVMs, which is between two and three orders of magnitude, allows the proposed hybrid WLS-SVC/HMM system to perform real-time speech decoding on a connected-digit recognition task (SpeechDat Spanish database). The experimental evaluation of the proposed system shows encouraging performance levels in clean and noisy conditions, although further improvements are required to reach the maturity level of current context-dependent HMM-based recognizers.

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Combining evidence from multiple classifiers for recognition of consonant-vowel units of speech in multiple languages

Cited by 9 publications

References 2 publications

Robust ASR using Support Vector Machines

Robust ASR using Support Vector Machines

Spotting and Recognition of Consonant-Vowel Units from Continuous Speech Using Accurate Detection of Vowel Onset Points

Real-Time Robust Automatic Speech Recognition Using Compact Support Vector Machines

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