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While vocal tract resonances (VTRs, or formants that are defined as such resonances) are known to play a critical role in human speech perception and in computer speech processing, there has been a lack of standard databases needed for the quantitative evaluation of automatic VTR extraction techniques. We report in this paper on our recent effort to create(More)
We present a stochastic mapping technique for robust speech recognition that uses stereo data. The idea is based on constructing a Gaussian mixture model for the joint distribution of the clean and noisy features and using this distribution to predict the clean speech during testing. The proposed mapping is called stereo-based stochastic mapping (SSM). Two(More)
This paper investigates data augmentation for deep neural network acoustic modeling based on label-preserving transformations to deal with data sparsity. Two data augmentation approaches, vocal tract length perturbation (VTLP) and stochastic feature mapping (SFM), are investigated for both deep neural networks (DNNs) and convolutional neural networks(More)
A feature compensation (FC) algorithm based on polynomial regression of utterance signal-to-noise ratio (SNR) for noise robust automatic speech recognition (ASR) is proposed. In this algorithm, the bias between clean and noisy speech features is approximated by a set of polynomials which are estimated from adaptation data from the new environment by the(More)
Automatic recognition of childrenÕs speech using acoustic models trained by adults results in poor performance due to differences in speech acoustics. These acoustical differences are a consequence of children having shorter vocal tracts and smaller vocal cords than adults. Hence, speaker adaptation needs to be performed. However, in real-world(More)
In this paper we describe the data collection for the TBALL project (Technology Based Assessment of Language and Literacy) and report the results of our efforts. We focus on aspects of our corpus that distinguish it from currently available corpora. The speakers are children (grades K-4), largely non-native speakers of English, and from diverse(More)
Spoken content in languages of emerging importance needs to be searchable to provide access to the underlying information. In this paper, we investigate the problem of extending data fusion methodologies from Information Retrieval for Spoken Term Detection on low-resource languages in the framework of the IARPA Babel program. We describe a number of(More)
Automatic speech recognition is a core component of many applications, including keyword search. In this paper we describe experiments on acoustic modeling, language modeling, and decoding for keyword search on a Cantonese conversational telephony corpus collected as part of the IARPA Babel program. We show that acoustic modeling techniques such as the(More)
Current hidden Markov acoustic modeling for large-vocabulary continuous speech recognition (LVCSR) heavily relies on the availability of abundant labeled transcriptions. Given that speech labeling is both expensive and time-consuming while there is a huge amount of unlabeled data easily available nowadays, the semi-supervised learning (SSL) from both(More)
Spectral mismatch between training and testing utterances can cause significant degradation in the performance of automatic speech recognition (ASR) systems. Speaker adaptation and speaker normalization techniques are usually applied to address this issue. One way to reduce spectral mismatch is to reshape the spectrum by aligning corresponding formant(More)