Silence Models in Weighted Finite-State Transducers

We investigate the effects of different silence modelling strategies in Weighted Finite-State Transducers for Automatic Speech Recognition. We show that the choice of silence models, and the way they are included in the transducer, can have a significant effect on the size of the resulting transducer; we present a means to prevent particularly large silence overheads. Our conclusions include that context-free silence modelling fits well with transducer based grammars, whereas modelling silence as a monophone and a context has larger overheads

To separate speech! a system for recognizing simultaneous speech

Abstract. The PASCAL Speech Separation Challenge (SSC) is based on a corpus of sentences from the Wall Street Journal task read by two speakers simultaneously and captured with two circular eight-channel microphone arrays. This work describes our system for the recognition of such simultaneous speech. Our system has four principal components: A person tracker returns the locations of both active speakers, as well as segmentation information for each utterance, which are often of unequal length; two beamformers in generalized sidelobe canceller (GSC) configuration separate the simultaneous speech by setting their active weight vectors according to a minimum mutual information (MMI) criterion; a postfilter and binary mask operating on the outputs of the beamformers further enhance the separated speech; and finally an automatic speech recognition (ASR) engine based on a weighted finite-state transducer (WFST) returns the most likely word hypotheses for the separated streams. In addition to optimizing each of these components, we investigated the effect of the filter bank design used to perform subband analysis and synthesis during beamforming. On the SSC development data, our system achieved a word error rate of 39.6%

A Weighted Finite State Transducer tutorial

The concepts of WFSTs are summarised, including structural and stochastic optimisations. A typical composition process for ASR is described. Some experiments show that care should be taken with silence models

The Juicer LVCSR Decoder - User Manual for Juicer version 0.5.0

Juicer is a decoder for HMM-based large vocabulary speech recognition that uses a weighted finite state transducer (WFST) representation of the search space. The package consists of a number of command line utilities: the Juicer decoder itself, along with a number of tools and scripts that are used to combine the various ASR knowledge sources (language model, pronunciation dictionary, acoustic models) into a single, optimised WFST that is input to the decoder

Learning commonsense human-language descriptions from temporal and spatial sensor-network data

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 105-109) and index.Embedded-sensor platforms are advancing toward such sophistication that they can differentiate between subtle actions. For example, when placed in a wristwatch, such platforms can tell whether a person is shaking hands or turning a doorknob. Sensors placed on objects in the environment now report many parameters, including object location, movement, sound, and temperature. A persistent problem, however, is the description of these sense data in meaningful human-language. This is an important problem that appears across domains ranging from organizational security surveillance to individual activity journaling. Previous models of activity recognition pigeon-hole descriptions into small, formal categories specified in advance; for example, location is often categorized as "at home" or "at the office." These models have not been able to adapt to the wider range of complex, dynamic, and idiosyncratic human activities. We hypothesize that the commonsense, semantically related, knowledge bases can be used to bootstrap learning algorithms for classifying and recognizing human activities from sensors.(cont.) Our system, LifeNet, is a first-person commonsense inference model, which consists of a graph with nodes drawn from a large repository of commonsense assertions expressed in human-language phrases. LifeNet is used to construct a mapping between streams of sensor data and partially ordered sequences of events, co-located in time and space. Further, by gathering sensor data in vivo, we are able to validate and extend the commonsense knowledge from which LifeNet is derived. LifeNet is evaluated in the context of its performance on a sensor-network platform distributed in an office environment. We hypothesize that mapping sensor data into LifeNet will act as a "semantic mirror" to meaningfully interpret sensory data into cohesive patterns in order to understand and predict human action.by Bo Morgan.S.M