Speech Separation by Humans and Machines

There is a serious problem in the recognition of sounds. It derives from the fact that they do not usually occur in isolation but in an environment in which a number of sound sources (voices, traffic, footsteps, music on the radio, and so on) are active at the same time. When these sounds arrive at the ear of the listener, the complex pressure waves coming from the separate sources add together to produce a single, more complex pressure wave that is the sum of the individual waves. The problem is how to form separate mental descriptions of the component sounds, despite the fact that the “mixture wave” does not directly reveal the waves that have been summed to form it. The name auditory scene analysis (ASA) refers to the process whereby the auditory systems of humans and other animals are able to solve this mixture problem. The process is believed to be quite general, not specific to speech sounds or any other type of sounds, and to exist in many species other than humans. It seems to involve assigning spectral energy to distinct “auditory objects” and “streams” that serve as the mental representations of distinct sound sources in the environment and the patterns that they make as they change over time. How this energy is assigned will affect the perceived n- ber of auditory sources, their perceived timbres, loudnesses, positions in space, and pitches.

Table of Contents:
Speech Segregation: Problems and Perspectives.- Auditory Scene Analysis.- Speech separation.- Recurrent Timing Nets for F0-based Speaker Separation.- Blind Source Separation Using Graphical Models.- Speech Recognizer Based Maximum Likelihood Beamforming.- Exploiting Redundancy to Construct Listening Systems.- Automatic Speech Processing by Inference in Generative Models.- Signal Separation Motivated by Human Auditory Perception: Applications to Automatic Speech Recognition.- Speech Segregation Using an Event-synchronous Auditory Image and STRAIGHT.- Underlying Principles of a High-quality Speech Manipulation System STRAIGHT and Its Application to Speech Segregation.- On Ideal Binary Mask As the Computational Goal of Auditory Scene Analysis.- The History and Future of CASA.- Techniques for Robust Speech Recognition in Noisy and Reverberant Conditions.- Source Separation, Localization, and Comprehension in Humans, Machines, and Human-machine Systems.- The Cancellation Principle in Acoustic Scene Analysis.- Informational and Energetic Masking Effects in Multitalker Speech Perception.- Masking the Feature Information In Multi-stream Speech-analogue Displays.- Interplay Between Visual and Audio Scene Analysis.- Evaluating Speech Separation Systems.- Making Sense of Everyday Speech: a Glimpsing Account.