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In this study, techniques for classification with missing or unreliable data are applied to the problem of noise-robustness in Automatic Speech Recognition (ASR). The techniques described make minimal assumptions about any noise background and rely instead on what is known about clean speech. A system is evaluated using the Aurora 2 connected digit(More)
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(More)
Distant microphone speech recognition systems that operate with human-like robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is rever-berantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech(More)
In previous work we h a ve developed the theory and demonstrated the promise of the Missing Data approach to robust Automatic Speech Recognition. This technique is based on hard decisions as to whether each time-frequency \pixel" is either reliable or unreliable. In this paper we replace these discrete decisions with soft estimates of the probability that(More)
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(More)
We present a new corpus designed for noise-robust speech processing research, CHiME. Our goal was to produce material which is both natural (derived from reverberant domestic environments with many simultaneous and unpredictable sound sources) and controlled (providing an enumerated range of SNRs spanning 20 dB). The corpus includes around 40 hours of(More)
Studies comparing native and non-native listener performance on speech perception tasks can distinguish the roles of general auditory and language-independent processes from those involving prior knowledge of a given language. Previous experiments have demonstrated a performance disparity between native and non-native listeners on tasks involving sentence(More)
In this study we describe two techniques for handling convolutional distortion with 'missing data' speech recognition using spectral features. The missing data approach to automatic speech recognition (ASR) is motivated by a model of human speech perception, and involves the modification of a hidden Markov model (HMM) classifier to deal with missing or(More)
This paper describes a perceptually motivated computational auditory scene analysis (CASA) system that combines sound separation according to spatial location with the "missing data" approach for robust speech recognition in noise. Missing data time-frequency masks are created using probability distributions based on estimates of interaural time and level(More)
In this work, techniques for classiication with missing or unreliable data are applied to the problem of noise-robustness in Automatic Speech Recognition (ASR). The primary advantage of this viewpoint is that it makes minimal assumptions about any noise background. As motivation, we review evidence that the auditory system is capable of dealing with(More)