Independent Component Analysis.

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

Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning to supervised speech separation has dramatically accelerated progress and boosted separation performance. This paper provides a comprehensive overview of the research on deep learning based supervised speech separation in the last several years. We first introduce the background of speech separation and the formulation of supervised separation. Then, we discuss three main components of supervised separation: learning machines, training targets, and acoustic features. Much of the overview is on separation algorithms where we review monaural methods, including speech enhancement (speech-nonspeech separation), speaker separation (multitalker separation), and speech dereverberation, as well as multimicrophone techniques. The important issue of generalization, unique to supervised learning, is discussed. This overview provides a historical perspective on how advances are made. In addition, we discuss a number of conceptual issues, including what constitutes the target source.

Supervised Speech Separation Based on Deep Learning: An Overview

http://ieeexplore.ieee.org/iel5/5/31047/01444179.pdf

Fundamentals of acoustics

The CHiME challenge series aims to advance far field speech recognition technology by promoting research at the interface of signal processing and automatic speech recognition. This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated scenario: a person talking to a tablet device that has been fitted with a six-channel microphone array. The paper describes the data collection, the task definition and the baseline systems for data simulation, enhancement and recognition. The paper then presents an overview of the 26 systems that were submitted to the challenge focusing on the strategies that proved to be most successful relative to the MVDR array processing and DNN acoustic modeling reference system. Challenge findings related to the role of simulated data in system training and evaluation are discussed.

/pdf/the-third-chime-speech-separation-and-recognition-challenge-4108e6hzs1.pdf

The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

Distant-microphone automatic speech recognition (ASR) remains a challenging goal in everyday environments involving multiple background sources and reverberation. This paper is intended to be a reference on the 2nd `CHiME' Challenge, an initiative designed to analyze and evaluate the performance of ASR systems in a real-world domestic environment. Two separate tracks have been proposed: a small-vocabulary task with small speaker movements and a medium-vocabulary task without speaker movements. We discuss the rationale for the challenge and provide a detailed description of the datasets, tasks and baseline performance results for each track.

/pdf/the-second-chime-speech-separation-and-recognition-challenge-l5fj9ahxts.pdf

The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines

The use of a microphone array for hands-free continuous speech recognition in noisy and reverberant environment is investigated. An array of eight omnidirectional microphones was placed at different angles and distances from the talker. A time delay compensation module was used to provide a beamformed signal as input to a hidden Markov model (HMM) based recognizer. A phone HMM adaptation, based on a small amount of phonetically rich sentences, further improved the recognition rate obtained by applying only beamforming. These results were confirmed both by experiments conducted in a noisy and reverberant environment and by simulations. In the latter case, different conditions were recreated by using the image method to reproduce synthetic versions of the array microphone signals.

Microphone array based speech recognition with different talker-array positions

A microphone array can be used to locate a dominant acoustic source in a given environment. This capability is successfully employed to locate an active talker in teleconferencing or other multi-speaker applications. In this work the source location is obtained in two steps: (1) a time difference of arrival (TDOA) computation between the signals of the array; (2) an "optimal" source location based on the interchannel delay estimates and on a geometrical description of the sensor arrangement. The crosspower spectrum phase technique was used for TDOA estimation, while a maximum likelihood approach was followed to derive the source coordinates. Source location experiments in a three-dimensional space were performed by means of an array of 8 microphones. For this purpose both a loudspeaker and a real talker were used to collect data in a large noisy and reverberant room.

Acoustic source location in a three-dimensional space using crosspower spectrum phase

Environmental conditions and acoustic transduction in hands-free speech recognition

Distant-microphone automatic speech recognition (ASR) remains a challenging goal in everyday environments involving multiple background sources and reverberation. This paper reports on the results of the 2nd `CHiME' Challenge, an initiative designed to analyse and evaluate the performance of ASR systems in a real-world domestic environment. We discuss the rationale for the challenge and provide a summary of the datasets, tasks and baseline systems. The paper overviews the systems that were entered for the two challenge tracks: small-vocabulary with moving talker and medium-vocabulary with stationary talker. We present a summary of the challenge findings including novel results produced by challenge system combination. Possible directions for future challenges are discussed.

/pdf/the-second-chime-speech-separation-and-recognition-challenge-3bljr1pe5k.pdf

Marco Matassoni

Papers

The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines

Microphone array based speech recognition with different talker-array positions

Acoustic source location in a three-dimensional space using crosspower spectrum phase

Environmental conditions and acoustic transduction in hands-free speech recognition

The second ‘CHiME’ speech separation and recognition challenge: An overview of challenge systems and outcomes