This paper describes the technical and system building advances made to the Google Home multichannel speech recognition system, which was launched in November 2016. Technical advances include an adaptive dereverberation frontend, the use of neural network models that do multichannel processing jointly with acoustic modeling, and Grid-LSTMs to model frequency variations. On the system level, improvements include adapting the model using Google Home specific data. We present results on a variety of multichannel sets. The combination of technical and system advances result in a reduction of WER of 8-28% relative compared to the current production system.

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SRI International is currently involved in the development of a new generation of software systems for automatic scoring of pronunciation as part of the Voice Interactive Language Training System (VILTS) project. This paper describes the goals of the VILTS system, the speech corpus, and the algorithm development. The automatic grading system uses SRI's Decipher™ continuous speech recognition system [1] to generate phonetic segmentations that are used to produce pronunciation scores at the end of each lesson. The scores produced by the system are similar to those of expert human listeners. Unlike previous approaches in which models were built for specific sentences or phrases, we present a new family of algorithms designed to perform well even when knowledge of the exact text to be used is not available.

WORD CORRECTNESSThis paper proposes a probabilistic framework t o define and evaluate confidence measures for word recognition. We describe a novel method to combine different knowledge sources and estimate the confidence in a word hypothesis, via a neural network. We also propose a measure of the joint performance of the recognition and confidence systems. The definitions and algorithms are illustrated with results on the Switchboard Corpus.