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Interspeech 20192019Machine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented dialogue systems from human-human conversations, which may be available in ample amounts in existing customer care center logs or can be collected from crowd workers. Annotating these datasets can be prohibitively expensive. Recently
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Interspeech 20192019In automatic speech recognition, confidence measures provide a quantitative representation used to assess the reliability of generated hypothesis text. For personal assistant devices like Alexa, speech recognition errors are inevitable due to the growing number of applications. Hence, confidence scores provide an important metric to downstream consumers to gauge the correctness of ASR hypothesis text and
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NAACL 20192019Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification. In many real-world scenarios, users have multiple intents in the same utterance, and a tokenlevel slot label can belong to more than one intent. We investigate an attention-based neural network model that performs multi-label classification for identifying multiple intents
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NAACL 20192019Text normalization (TN) is an important step in conversational systems. It converts written text to its spoken form to facilitate speech recognition, natural language understanding and text-to-speech synthesis. Finite state transducers (FSTs) are commonly used to build grammars that handle text normalization (Sproat, 1996; Roark et al., 2012). However, translating linguistic knowledge into grammars requires
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IEEE Journal on Emerging and Selected Topics in Circuits and System (JETCAS)2019Large scale machine learning (ML) systems such as the Alexa automatic speech recognition (ASR) system continue to improve with increasing amounts of manually transcribed training data. Instead of scaling manual transcription to impractical levels, we utilize semi-supervised learning (SSL) to learn acoustic models (AM) from the vast firehose of untranscribed audio data. Learning an AM from 1 Million hours
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January 22, 2019Developing a new natural-language-understanding system usually requires training it on thousands of sample utterances, which can be costly and time-consuming to collect and annotate. That’s particularly burdensome for small developers, like many who have contributed to the library of more than 70,000 third-party skills now available for Alexa. -
Projection image adapted from Michael Horvath under the CC BY-SA 4.0 licenseJanuary 15, 2019Neural networks have been responsible for most of the top-performing AI systems of the past decade, but they tend to be big, which means they tend to be slow. That’s a problem for systems like Alexa, which depend on neural networks to process spoken requests in real time.
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December 21, 2018In May 2018, Amazon launched Alexa’s Remember This feature, which enables customers to store “memories” (“Alexa, remember that I took Ben’s watch to the repair store”) and recall them later by asking open-ended questions (“Alexa, where is Ben’s watch?”).
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December 18, 2018At a recent press event on Alexa's latest features, Alexa’s head scientist, Rohit Prasad, mentioned multistep requests in one shot, a capability that allows you to ask Alexa to do multiple things at once. For example, you might say, “Alexa, add bananas, peanut butter, and paper towels to my shopping list.” Alexa should intelligently figure out that “peanut butter” and “paper towels” name two items, not four, and that bananas are a separate item.
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December 17, 2018In recent years, data representation has emerged as an important research topic within machine learning.
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December 13, 2018Language models are a key component of automatic speech recognition systems, which convert speech into text. A language model captures the statistical likelihood of any particular string of words, so it can help decide between different interpretations of the same sequence of sounds.
