Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …

A language model is modified for a local speech recognition system using remote speech
recognition sources. In one example, a speech utterance is received. The speech utterance …

We present a novel approach to query-by-example keyword spotting (KWS) using a long
short-term memory (LSTM) recurrent neural network-based feature extractor. In our …

In this paper we investigate a time delay neural network (TDNN) for a keyword spotting task
that requires low CPU, memory and latency. The TDNN is trained with transfer learning and …

We propose a max-pooling based loss function for training Long Short-Term Memory
(LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and …

Spoken content retrieval refers to directly indexing and retrieving spoken content based on
the audio rather than text descriptions. This potentially eliminates the requirement of …

Accurate on-device wake word detection is crucial to products with far-field voice control
such as the Amazon Echo. It is quite challenging to build a wake word system with both low …

We propose a neural network model to estimate word translation probabilities for Cross-
Lingual Information Retrieval (CLIR). The model estimates better probabilities for word …

The IARPA BABEL program has stimulated worldwide research in keyword search
technology for low resource languages, and the NIST OpenKWS evaluations are the de …

Existing research suggests that automatic speech recognition (ASR) models can benefit
from additional contexts (eg, contact lists, user specified vocabulary). Rare words and …