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Interspeech 20162016Several consumer speech devices feature voice interfaces that perform on-device keyword spotting to initiate user interactions. Accurate on-device keyword spotting within a tight CPU budget is crucial for such devices. Motivated by this, we investigated two ways to improve deep neural network (DNN) acoustic models for keyword spotting without increasing CPU usage. First, we used low-rank weight matrices
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Interspeech 20162016We present a new model called LATTICERNN, which generalizes recurrent neural networks (RNNs) to process weighted lattices as input, instead of sequences. A LATTICERNN can encode the complete structure of a lattice into a dense representation, which makes it suitable to a variety of problems, including rescoring, classifying, parsing, or translating lattices using deep neural networks (DNNs). In this paper
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Interspeech 20162016The goal of this paper is to simulate the benefits of jointly applying active learning (AL) and semi-supervised training (SST) in a new speech recognition application. Our data selection approach relies on confidence filtering, and its impact on both the acoustic and language models (AM and LM) is studied. While AL is known to be beneficial to AM training, we show that it also carries out substantial improvements
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Interspeech 20152015In an online automatic speech recognition system, the role of the endpoint detector is to infer when a user has finished speaking a query. Accurate and low-latency endpoint detection is crucial for natural voice interaction. Classic voice activity detector (VAD) based approaches monitor the incoming audio and trigger when a sufficiently long pause is detected. Such approaches are typically limited due to
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Interspeech 20152015In the past, conventional i-vectors based on a Universal Background Model (UBM) have been successfully used as input features to adapt a Deep Neural Network (DNN) Acoustic Model (AM) for Automatic Speech Recognition (ASR). In contrast, this paper introduces Hidden Markov Model (HMM) based ivectors that use HMM state alignment information from an ASR system for estimating i-vectors. Further, we propose passing
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April 25, 2019When a customer asks Alexa to play “Hey Jude”, and Alexa responds, “Playing 'Hey Jude' by the Beatles,” that response is generated by a text-to-speech (TTS) system, which converts textual inputs into synthetic-speech outputs... -
April 22, 2019One of the ways that we’re always trying to improve Alexa’s performance is by teaching her to ignore speech that isn’t intended for her. At this year’s International Conference on Acoustics, Speech, and Signal Processing, my colleagues and I will present a new technique for doing this, which could complement the techniques that Alexa already uses.
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April 18, 2019Last year, Amazon announced the beta release of Alexa Guard, a new service that lets customers who are leaving the house instruct their Echo devices to listen for glass breaking or smoke and carbon dioxide alarms going off. At this year’s International Conference on Acoustics, Speech, and Signal Processing, our team is presenting several papers on sound detection. I wrote about one of them a few weeks ago, a new method for doing machine learning with unbalanced data sets.
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April 11, 2019Multiband dynamics processing, which separately modifies volume in different frequency bands of an audio signal, is known to improve listeners’ audio experiences. But in the context of voice-controlled systems like the Amazon Echo family of products, it can also improve automatic speech recognition by making echo cancellation easier.
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April 8, 2019Transfer learning is the technique of adapting a machine learning model trained on abundant data to a new context in which training data is sparse. On the Alexa team, we’ve explored transfer learning as a way to bootstrap new functions and to add new classification categories to existing machine learning systems.
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April 4, 2019Customer interactions with Alexa are constantly growing more complex, and on the Alexa science team, we strive to stay ahead of the curve by continuously improving Alexa’s speech recognition system. Increasingly, keeping pace with Alexa’s expanding capabilities will require automating the learning process, through techniques such as semi-supervised learning, which leverages a small amount of annotated data to extract information from a much larger store of unannotated data.
