Anirudh Raju

TL;DR: This work proposes a max-pooling based loss function for training Long Short-Term Memory networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements and results show that LSTM models trained using cross-entropy loss or max- Pooling loss outperform a cross-ENTropy loss trained baseline feed-forward Deep Neural Network (DNN).

TL;DR: This paper proposes a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement, and believes that this work is a step towards an automatic evaluation process for conversational AIs.

TL;DR: This work proposes to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle.

TL;DR: This paper adopted the teacher-student learning technique using a parallel clean and noisy corpus for improving automatic speech recognition (ASR) performance under multimedia noise and applied a logits selection method which only preserves the k highest values to prevent wrong emphasis of knowledge from the teacher and to reduce bandwidth needed for transferring data.

TL;DR: Experimental results show that the TDB-HW network with the complex DFT feature provides significantly lower miss rates for a range of false alarm rates over the LFBE DNN, yielding approximately 20 % relative improvement in the area under the curve (AUC) of the detection error tradeoff (DET) curves for keyword spotting.