Language barrier is the primary challenge for effective cross-lingual conversations. Spoken
language translation (SLT) is perceived as a cost-effective alternative to less affordable …

Detection of automatic speech recognition (ASR) errors is crucial to preventing their further
propagation through statistical machine translation (SMT) in conversational spoken …

Smart speakers with voice assistants like Google Home or Amazon Alexa are increasingly
popular and essential in our daily lives due to their convenience of issuing voice commands …

Many goal-oriented dialog agents are expected to identify slot-value pairs in a spoken
query, then perform lookup in a knowledge base to complete the task. When the agent …

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 …

Abstract Voice User Interfaces (VUI) are rapidly gaining popularity, revolutionizing user
interaction with technology through the widespread adoption in devices such as desktop …

To respond to a user's query, dialog agents can use a knowledge base that is either domain
specific, commonsense (eg, NELL, Freebase) or a combination of both. The drawback is that …

In this paper we describe progress we have made in detecting out-of-vocabulary words
(OOVs) for a speech-to-speech translation system for the purpose of playing back audio to …

Out-of-vocabulary named entities (OOV NEs) are always misrecognized by fixed-vocabulary
automatic speech recognition (ASR) systems. This has a negative impact on downstream …

In this paper, we propose a robust neural machine translation (NMT) framework to deal with
homophone errors. The framework consists of a homophone noise detector and a syllable …