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We describe a language identification system developed for robustess to noise conditions such as those encountered under the DARPA RATS program, which is focused on multi-channel audio collected in high noise conditions.

Aug, 2013
In Proceedings
413

This paper presents a new signal-processing technique motivated by the physiology of human auditory system. In this approach, auditory hair cells are modeled as damped oscillators that are stimulated by bandlimited time domain speech signals acting as forcing functions.

Aug, 2013
In Proceedings
413

This paper proposes adaptive Gaussian backend (AGB), a novel approach to robust language identification (LID).

Aug, 2013
In Proceedings
413

We address the problem of retrieving spoken information from noisy and heterogeneous audio archives using a rich system combination with a diverse set of noise-robust modules and audio characterization.

May, 2013
In Proceedings
413

We present a neural network model to estimate articulatory trajectories from speech signals where the model was trained using synthetic speech signals generated by Haskins Laboratories’ task-dynamic model of speech production.Acoustic Modeling for Automatic Speech Recognition.

May, 2013
In Proceedings
413

The SRI team joined the subtask of Chinese-English Patent machine translation evaluation, and submitted the translation results using a combined output from two types of grammars supported in SRlnterp, with two different word segmentations.

May, 2013
In Proceedings
413

This study investigates the use of multiple versions of the same speech unit in automatic phone recognition.

May, 2013
In Proceedings
413

We propose a novel approach for noise-robust speaker recognition, where the model of distortions caused by additive and convolutive noises is integrated into the i-vector extraction framework.

May, 2013
In Proceedings
413

Improving the robustness of speech recognition systems to cope with adverse background noise is a challenging research topic.

May, 2013
In Proceedings
413

We present a novel approach for improving communication success between users of speech-to-speech translation systems by automatically detecting errors in the output of automatic speech recognition (ASR) and statistical machine translation (SMT) systems.

May, 2013
In Proceedings
413

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