Improved automatic language identification in noisy speech

Goodman, Fred; Martin, Arnaud; Wohlford, R.

doi:10.1109/icassp.1989.266480

Cited by 27 publications

(7 citation statements)

References 2 publications

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“…Early approaches that exploited the acoustic feature vectors included a filter bandbased approach (Leonard & Doddington, 1974), LPC-based polynomial classification approach (Cimarusti & Ives, 1982), and format vector quantization-based approaches (Foil, 1986;Goodman, 1989). House and Neuburg (1977) published the earliest language identification approach based on the different phonotactic constraints of different spoken languages.…”

Section: Introductionmentioning

confidence: 99%

Development of an approach to automatic language identification based on phone recognition

Barnard

Cole

1996

Computer Speech & Language

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Section: Introductionmentioning

confidence: 99%

Development of an approach to automatic language identification based on phone recognition

Barnard

Cole

1996

Computer Speech & Language

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“…His formant vector based language ID system used k-means training and vector quantization classification. Goodman [3] extended Foil's work by refining the formant feature vector and classification distance metric. Ives [8] constructed a rule-based ID system.…”

Section: Introductionmentioning

confidence: 99%

Automatic language identification using Gaussian mixture and hidden Markov models

Zissman

1993

IEEE International Conference on Acoustics Speech and Signal Processing

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Ergodic, continuous-observation, hidden Markov models (HMMs) were used to perform automatic language classification and detection of speech messages. State observation probability densities were modeled as tied Gaussian mixtures. The algorithm was evaluated on four multilanguage speech databases: a three language subset of the Spoken Language Library, a three language subset of a five language Rome Laboratory database, the 20 language CCITT database, and the ten language OGI telephone speech database. Generally, performance of a single state HMM (i.e, a static Gaussian mixture classifier comparable to the multistate HMMs, indicating that t h ; : quential modeling capabilities of HMMs were not exploited.

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“…He achieved an accuracy of 64% with short speech segments after applying Markov modelling technique in his experiment. Goodman [11] extended Foil's work by developing a new distance metric, modifying the parameter sets, and adding a new correlation-based voicing statistic. This reduced the error rate obtained by Foil by more than 50%.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

et al. 2020

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This era is dominated by artificial intelligence and its various applications-one of which is Spoken Language Identification (S-LID) which has always been a challenging issue and an important research area in the domain of speech signal processing. This paper deals with SLID to be used for Human-Computer Interaction (HCI) based applications by attempting to classify various languages from three multilingual databases namely CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages, VoxForge and Indian Institute of Technology, Madras (IIT-Madras) speech corpus database by extracting their Mel-Spectrogram features and Relative Spectral Transform-Perceptual Linear Prediction (RASTA-PLP) features. A new hybrid Feature Selection (FS) algorithm have been developed using the versatile Harmony Search (HS) algorithm and a new nature-inspired algorithm called Naked Mole-Rat (NMR) algorithm to select the best subset of features and reduce the model complexity to help it train faster. This selected feature set is fed to five classifiers namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Multi-layer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF). The evaluation measures used in this paper are precision, recall, f1-score, classification accuracy and number of selected features. An accuracy of 99.89% on CSS10, 98.22% on VoxForge and 99.75% on IIT-Madras speech corpus databases is achieved using RF. Furthermore, the proposed algorithm is found to outperform 15 standard meta-heuristic FS algorithms.

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Improved automatic language identification in noisy speech

Cited by 27 publications

References 2 publications

Development of an approach to automatic language identification based on phone recognition

Development of an approach to automatic language identification based on phone recognition

Automatic language identification using Gaussian mixture and hidden Markov models

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

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