Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Speech recognition in noisy environments: a survey
Speech Communication
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Prediction-driven computational auditory scene analysis
Prediction-driven computational auditory scene analysis
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Speech separation using speaker-adapted eigenvoice speech models
Computer Speech and Language
Monaural speech separation based on MAXVQ and CASA for robust speech recognition
Computer Speech and Language
Super-human multi-talker speech recognition: A graphical modeling approach
Computer Speech and Language
A computational auditory scene analysis system for speech segregation and robust speech recognition
Computer Speech and Language
Speech fragment decoding techniques for simultaneous speaker identification and speech recognition
Computer Speech and Language
First stereo audio source separation evaluation campaign: data, algorithms and results
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Super-human multi-talker speech recognition: A graphical modeling approach
Computer Speech and Language
Speech fragment decoding techniques for simultaneous speaker identification and speech recognition
Computer Speech and Language
The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Trends and advances in speech recognition
IBM Journal of Research and Development
The Markov selection model for concurrent speech recognition
Neurocomputing
Disordered voice measurement and auditory analysis
Speech Communication
A non-negative approach to language informed speech separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
The 2011 signal separation evaluation campaign (SiSEC2011): - audio source separation -
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Journal of Signal Processing Systems
The PASCAL CHiME speech separation and recognition challenge
Computer Speech and Language
Blind source extraction for robust speech recognition in multisource noisy environments
Computer Speech and Language
Computer Speech and Language
Multi-pitch Streaming of Harmonic Sound Mixtures
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Robust speech recognition in everyday conditions requires the solution to a number of challenging problems, not least the ability to handle multiple sound sources. The specific case of speech recognition in the presence of a competing talker has been studied for several decades, resulting in a number of quite distinct algorithmic solutions whose focus ranges from modeling both target and competing speech to speech separation using auditory grouping principles. The purpose of the monaural speech separation and recognition challenge was to permit a large-scale comparison of techniques for the competing talker problem. The task was to identify keywords in sentences spoken by a target talker when mixed into a single channel with a background talker speaking similar sentences. Ten independent sets of results were contributed, alongside a baseline recognition system. Performance was evaluated using common training and test data and common metrics. Listeners' performance in the same task was also measured. This paper describes the challenge problem, compares the performance of the contributed algorithms, and discusses the factors which distinguish the systems. One highlight of the comparison was the finding that several systems achieved near-human performance in some conditions, and one out-performed listeners overall.