Amazon Technologies, Inc.

Roland Maximilian Rolf Maas

Ariya Rastrow

Bjorn Hoffmeister

Eli Joshua Fidler

Nikko Strom

Ankur Gandhe

Aaron A Eakin

Diamond Bishop

Veerdhawal Pande

Sanjeev Mishra

James G Droppo

Spyridon Matsoukas

Kevin Crews

Andrew Smith

Gautam Tiwari

Andrew Oberlin

Samarth Batra

Prasanna H Sridhar

Paul Anthony Bernhardt

Maarten Van Segbroeck

Sri Harish Reddy Mallidi

Nicholas Matthew Jutila

A speech interface device is configured to detect an interrupt event and process a voice command without detecting a wakeword. The device includes on-device interrupt architecture configured to detect when device-directed speech is present and send audio data to a remote system for speech processing. This architecture includes an interrupt detector that detects an interrupt event (e.g., device-directed speech) with low latency, enabling the device to quickly lower a volume of output audio and/or perform other actions in response to a potential voice command. In addition, the architecture includes a device directed classifier that processes an entire utterance and corresponding semantic information and detects device-directed speech with high accuracy. Using the device directed classifier, the device may reject the interrupt event and increase a volume of the output audio or may accept the interrupt event, causing the output audio to end and performing speech processing on the audio data.

Device-directed utterance detection

A speech-processing system may determine potential endpoints in a user's speech. Such endpoint prediction may include determining a potential endpoint in a stream of audio data, and may additionally including determining an endpoint score representing a likelihood that the potential endpoint represents an end of speech representing a complete user input. When the potential endpoint has been determined, the system may publish a transcript of speech that preceded the potential endpoint, and send it to downstream components. The system may continue to transcribe audio data and determine additional potential endpoints while the downstream components process the transcript. The downstream components may determine whether the transcript is complete; e.g., represents the entirety of the user input. Final endpoint determinations may be made based on the results of the downstream processing including automatic speech recognition, natural language understanding, etc.

Endpointing in speech processing

Exemplary embodiments relate to adapting a generic language model during runtime using domain-specific language model data. The system performs an audio frame-level analysis, to determine if the utterance corresponds to a particular domain and whether the ASR hypothesis needs to be rescored. The system processes, using a trained classifier, the ASR hypothesis (a partial hypothesis) generated for the audio data processed so far. The system determines whether to rescore the hypothesis after every few audio frames (representing a word in the utterance) are processed by the speech recognition system.

Language model adaptation

Techniques for an interactive turn-based reading experience are described. A system may take turns reading content, such as a book, with a user. The system may process audio data representing a user reading a portion of the content, determine reading evaluation data, and determine how to proceed for the next turn based on the reading evaluation data. For example, based on the reading evaluation data, the system may read a portion of the content by outputting synthesized speech representing the content, may ask the user re-read a portion of the content, or may ask the user to read a different, smaller portion of the content.

Speech processing techniques

A speech-processing system capable of receiving and processing audio data to determine if the audio data includes speech that was intended for the system. Non-system directed speech may be filtered out while system-directed speech may be selected for further processing. A system-directed speech detector may use a trained machine learning model (such as a deep neural network or the like) to process a feature vector representing a variety of characteristics of the incoming audio data, including the results of automatic speech recognition and/or other data. Using the feature vector the model may output an indicator as to whether the speech is system-directed. The system may also incorporate other filters such as voice activity detection prior to speech recognition, or the like.

Detecting system-directed speech

Growing community of inventors

Seattle, WA, United States of America

Roland Maximilian Rolf Maas