-
Interspeech 20192019We present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that
-
NAACL 20192019The task of Natural Language Inference (NLI) is widely modeled as supervised sentence pair classification. While there has been a lot of work recently on generating explanations of the predictions of classifiers on a single piece of text, there have been no attempts to generate explanations of classifiers operating on pairs of sentences. In this paper, we show that it is possible to generate token-level
-
ACL 20192019This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms. While being effective, these constrained decoding methods add, however, significant computational overhead to the inference step, and, as we show
-
Interspeech 20192019Grapheme-to-phoneme (G2P) models are a key component in Automatic Speech Recognition (ASR) systems, such as the ASR system in Alexa, as they are used to generate pronunciations for out-of-vocabulary words that do not exist in the pronunciation lexicons (mappings like ”e c h o” → ”E k oU”). Most G2P systems are monolingual and based on traditional joint-sequence-based n-gram models. As an alternative, we
-
SIGDIAL 20192019In a spoken-dialogue system, dialogue state tracker (DST) components track the state of the conversation by updating a distribution of values associated with each of the slots being tracked for the current user turn, using the interactions until then. Much of the previous work has relied on modeling the natural order of the conversation, using distance based offsets as an approximation of time. In this
Related content
-
March 11, 2021Watch a recording of the presentation and Q&A roundtable featuring Amazon scientists and scholars.
-
March 11, 2021University teams will compete in building agents that can help customers complete complex tasks, like cooking and home improvement. Deadline for university team applications is April 16.
-
March 02, 2021The newest chapter addresses a problem that often bedevils nonparametric machine learning models.
-
March 01, 2021The Art Museum skill uses Alexa Conversations, an AI-driven dialogue management tool.
-
February 08, 2021Technique that relies on inverse reinforcement learning, or learning by example, improves task completion rate by 14% to 17% in simulations.
-
February 08, 2021Yanagisawa discusses the science behind Alexa's new bilingual Polyglot model, her career in speech research, and more.