An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

An electronic device may capture a voice command from a user. The electronic device may store contextual information about the state of the electronic device when the voice command is received. The electronic device may transmit the voice command and the contextual information to computing equipment such as a desktop computer or a remote server. The computing equipment may perform a speech recognition operation on the voice command and may process the contextual information. The computing equipment may respond to the voice command. The computing equipment may also transmit information to the electronic device that allows the electronic device to respond to the voice command.

Electronic Devices with Voice Command and Contextual Data Processing Capabilities

A method for operating a voice trigger is provided. In some implementations, the method is performed at an electronic device including one or more processors and memory storing instructions for execution by the one or more processors. The method includes receiving a sound input. The sound input may correspond to a spoken word or phrase, or a portion thereof. The method includes determining whether at least a portion of the sound input corresponds to a predetermined type of sound, such as a human voice. The method includes, upon a determination that at least a portion of the sound input corresponds to the predetermined type, determining whether the sound input includes predetermined content, such as a predetermined trigger word or phrase. The method also includes, upon a determination that the sound input includes the predetermined content, initiating a speech-based service, such as a voice-based digital assistant.

Voice trigger for a digital assistant

A user request is received from a mobile client device, where the user request includes at least a speech input and seeks an informational answer or performance of a task. A failure to provide a satisfactory response to the user request is detected. In response to detection of the failure, information relevant to the user request is crowd-sourced by querying one or more crowd sourcing information sources. One or more answers are received from the crowd sourcing information sources, and the response to the user request is generated based on at least one of the one or more answers received from the one or more crowd sourcing information sources.

Crowd sourcing information to fulfill user requests

We present a new hardware adaptive filter architecture for very high throughput LMS adaptive filters using distributed arithmetic (DA). DA uses bit-serial operations and look-up tables (LUTs) to implement high throughput filters that use only about one cycle per bit of resolution regardless of filter length. However, building adaptive DA filters requires recalculating the LUTs for each adaptation which can negate any performance advantages of DA filtering. By using an auxiliary LUT with special addressing, the efficiency and throughput of DA adaptive filters can be of the same order as fixed DA filters. In this paper, we discuss a new hardware adaptive filter structure for very high throughput LMS adaptive filters. We describe the development of DA adaptive filters and show that practical implementations of DA adaptive filters have very high throughput relative to multiply and accumulate architectures. We also show that DA adaptive filters have a potential area and power consumption advantage over digital signal processing microprocessor architectures.

LMS adaptive filters using distributed arithmetic for high throughput

In speech processing systems, compensation is made for sudden changes in the background noise in the average signal-to-noise ratio (SNR) calculation SNR outlier filtering may be used, alone or in conjunction with weighting the average SNR Adaptive weights may be applied on the SNRs per band before computing the average SNR The weighting function can be a function of noise level, noise type, and/or instantaneous SNR value Another weighting mechanism applies a null filtering or outlier filtering which sets the weight in a particular band to be zero This particular band may be characterized as the one that exhibits an SNR that is several times higher than the SNRs in other bands

Voice activity detection in presence of background noise

The range of disclosed configurations includes methods in which subbands of a speech signal are separately encoded, with the excitation of a first subband being derived from a second subband. Gain factors are calculated to indicate a time-varying relation between envelopes of the original first subband and of the synthesized first subband. The gain factors are quantized, and quantized values that exceed the pre-quantized values are re-coded.

Systems, methods, and apparatus for gain factor limiting

In this paper, the latest wideband vocoder standard adopted by the cdma2000 standardization body, 3GPP2, is described. Christened enhanced variable rate codec-wideband (EVRC-WB), the proposed codec encodes wideband speech (16 KHz sampling frequency) at a maximum bit-rate of 8.55 kbit/s. EVRC-WB is based on a split band coding paradigm in which two different coding models are used for the signal components in the low frequency (LF) (0-4 KHz) and the high frequency (HF) (3.5-7 KHz) bands. The coding model used for the former is based on the EVRC-B narrowband (0-4 KHz) codec, modified to encode the LF band signal at a maximum bitrate of 7.75 kbit/s. The HF band coding model is a LPC based coding scheme where the excitation is derived from the coded LF band excitation using non-linear processing. Mean opinion scores from 3GPP2 characterization tests are provided to demonstrate that the EVRC-WB codec (8.55 kbit/s, max.) performs statistically significantly better than the adaptive multirate wideband (12.65 kbit/s, max.).

EVRC-Wideband: The New 3GPP2 Wideband Vocoder Standard

A method for modifying a window with a frame associated with an audio signal is described. A signal is received. The signal is partitioned into a plurality of frames. A determination is made if a frame within the plurality of frames is associated with a non-speech signal. A modified discrete cosine transform (MDCT) window function is applied to the frame to generate a first zero pad region and a second zero pad region if it was determined that the frame is associated with a non-speech signal. The frame is encoded. The decoder window is the same as the encoder window.

Venkatesh Krishnan

Papers

LMS adaptive filters using distributed arithmetic for high throughput

Voice activity detection in presence of background noise

Systems, methods, and apparatus for gain factor limiting

EVRC-Wideband: The New 3GPP2 Wideband Vocoder Standard

Systems and methods for modifying a window with a frame associated with an audio signal