Building on multimodal embedding techniques, we show that data augmentation via two distinct approaches improves results: entity linking and cross-domain local similarity scaling.
Wideband Spectral Monitoring Using Deep Learning
We present a system to perform spectral monitoring of a wide band of 666.5 MHz, located within a range of 6 GHz of Radio Frequency (RF) bandwidth, using state-of-the-art deep learning approaches.
Robust Speaker Recognition from Distant Speech under Real Reverberant Environments Using Speaker Embeddings
This article focuses on speaker recognition using speech acquired using a single distant or far-field microphone in an indoors environment.
Speech recognition in unseen and noisy channel conditions
This work investigates robust features, feature-space maximum likelihood linear regression (fMLLR) transform, and deep convolutional nets to address the problem of unseen channel and noise conditions in speech recognition.
The SRI System for the NIST OpenSAD 2015 Speech Activity Detection Evaluation
In this paper, we present the SRI system submission to the NIST OpenSAD 2015 speech activity detection (SAD) evaluation. We present results on three different development databases that we created from the provided data.
Minimizing Annotation Effort for Adaptation of Speech-Activity Detection Systems
This paper focuses on the problem of selecting the best-possible subset of available audio data given a budgeted time for annotation.
A Phonetically Aware System for Speech Activity Detection
In this paper, we focus on a dataset of highly degraded signals, developed under the DARPA Robust Automatic Transcription of Speech (RATS) program.
Improving robustness against reverberation for automatic speech recognition
In this work, we explore the role of robust acoustic features motivated by human speech perception studies, for building ASR systems robust to reverberation effects.
Mitigating the effects of non-stationary unseen noises on language recognition performance
We introduce a new dataset for the study of the effect of highly non-stationary noises on language recognition (LR) performance.