TIMIT

About: TIMIT is a research topic. Over the lifetime, 1401 publications have been published within this topic receiving 59888 citations. The topic is also known as: TIMIT Acoustic-Phonetic Continuous Speech Corpus.

Fuzzy rule selection using Iterative Rule Learning for speech data classification

Abstract: Fuzzy rule-based systems have been successfully used for pattern classification. These systems focus on generating a rule-base from numerical input data. The resulting rule-base can be applied on classification problems. However, we are faced with some challenges when generating and selecting the appropriate rules to create final rule-base. In this paper, a novel approach for rule selection is proposed. The proposed algorithm makes the use of iterative rule learning (IRL) to reduce the search space of the classification problem in hand for rule-base extraction. The major element of our proposed approach is an evaluation metric which is able to accurately estimate the degree of cooperation of the candidate rule with current rules in the rule-base. Finally, fine-tuning of the selected rules is handled by employing a proposed rule-weighting mechanism. To evaluate the performance of the proposed scheme, TIMIT speech corpus was utilized for framewise classification of speech data. The results show the effectiveness of the proposed method while preserving the interpretability of the classification results.

A class-specific speech enhancement for phoneme recognition: a dictionary learning approach

Abstract: We study the influence of using class-specific dictionaries for enhancement over class-independent dictionary in phoneme recognition of noisy speech. We hypothesize that, using class specific dictionaries would remove the noise more compared to a class-independent dictionary, thereby resulting in better phoneme recognition. Experiments are performed with speech data from TIMIT corpus and noise samples from NOISEX-92 database. Using KSVD, four types of dictionaries have been learned: class-independent, manner-of-articulation-class, place-of-articulation-class and 39 phoneme-class. Initially, a set of labels are obtained by recognizing the speech, enhanced using a class-independent dictionary. Using these approximate labels, the corresponding class-specific dictionaries are used to enhance each frame of the original noisy speech, and this enhanced speech is then recognized. Compared to the results obtained using the class-independent dictionary, the 39 phoneme class based dictionaries provide a relative phoneme recognition accuracy improvement of 5.5%, 3.7%, 2.4% and 2.2%, respectively for factory2, m109, leopard and babble noises, when averaged over 0, 5 and 10 dB SNRs.

Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees

Abstract: We propose a novel approach to generate an ensemble of context-dependent deep neural networks (CD-DNNs) by using random forests of phonetic decision trees (RF-PDTs) and construct an ensemble acoustic model (EAM) accordingly for speech recognition. We present evaluation results on the TIMIT dataset and a telemedicine automatic captioning dataset and demonstrate the superior performance of the proposed RF-PDT+CD-DNN based EAM over the conventional CD-DNN based single acoustic model (SAM) in phone and word recognition accuracies.

Statistics based features for unvoiced sound classification

Abstract: Unvoiced phonemes have significant presence in spoken English language. These phonemes are hard to classify, due to their weak energy and lack of periodicity. Sound textures such as sound made by a flowing stream of water or falling droplets of rain have similar aperiodic properties in temporal domain as unvoiced phonemes. These sounds are easily differentiated by a human ear. Recent studies on sound texture analysis and synthesis have shown that the human auditory system perceives sound textures using simple statistics. These statistics are obtained by decomposing sounds using a set of filter-banks and computing the moments of the filter responses, along with their correlation values. In this work we investigate if the above mentioned statistics, which are easy to extract, can also be used as features for classifying unvoiced sounds. To incorporate the moments and correlation values as features, a framework containing multiple classifiers is proposed. Experiments conducted on the TIMIT dataset gave an accuracy on par with the latest reported in the literature, with lesser computational cost.