1,721,007 research outputs found
A Multi-Objective Programming-Based Approach to Language Model Adaptation
No full textIn this paper, we present a multi-layer learning approach to the language model (LM) adaptation problem by making use of multi-objective programming (MOP). The overall objective function of conventional MAP-based LM adaptation is implicitly a composition of two objective functions: The first objective is concerned with the maximum likelihood estimation of the model parameters from the indomain data while the second objective is concerned with an appropriate representation of prior information obtained from a general purpose corpus. In this paper, we separate these individual objective functions, which are at least partially conflicting, and take an MOP approach to LM adaptation. The resulting MOP problem is solved in an iterative manner such that each objective is optimized one after another with constraints on the others. This iterative solution can be represented as a multi-layer learning problem in each layer of which only one objective is minimized with constraints on others. In estimating an n-gram LM, number of the layers is given by 2× n with one hidden unit per layer. The inputs to the hidden units are LMs of order up to n that are estimated either from the general purpose corpus or from the in-domain data. When solved this way, the target LM is in the form of a log-linear interpolation of component LMs. In our preliminary experiments with bigram LMs, the proposed approach slightly outperformed linear interpolation. In our ongoing work with trigram LMs, we expect the proposed approach to outperform linear interpolation in terms of both the perplexity and the automatic speech recognition work error rate
An experimental study on continuous phone recognition with little or no language specific-training data
No full textWe study continuous phone recognition with little or no language-specific speech training data. The phone recognizer integrates three levels of information from: (1) frame based speech attribute detectors, (2) artificial neural network based phone event mergers, and (3) decoding based evidence verifiers. With a set of acoustic phonetic attributes defined over a number of available languages, a collection of attribute-to-phone mapping rules can either be specified in a language-dependent way, one for each language, or even independently for all languages if the attribute specification is complete to cover all phones and the phone definition is universal to cover all spoken languages. We report on experimental results on Japanese phone recognition with the OGI Multilingual Speech Corpus. It is interesting that a good performance can be achieved without using any Japanese speech training data, and the phone accuracy rates vary depending on how the attribute detectors and phone mergers are configured. Further improvement is observed by adding little Japanese data to train the attribute-to-phone mergers
A study on lattice rescoring with knowledge scores for automatic speech recognition
No full textWe study lattice rescoring with knowledge scores for automatic speech recognition. Frame-based log likelihood ratio is adopted as a score measure of the goodness-of-fit between a speech segment and the knowledge sources. We evaluate our approach in two different applications: phone recognition, and connected digit continuous recognition. By incorporating knowledge scores obtained from 15 attribute detectors for place and manner of articulation, we reduced phone error rate from 40.52% to 35.16% using monophone models. The error rate can be further reduced to 33.42% for triphone models. The same lattice rescoring algorithm is extended to connected digit recognition using the TIDIGITS database, and without using any digit-specific training data. We observed the digit error rate can be effectively reduced to 4.03% from 4.54% which was obtained with the conventional Viterbi decoding algorithm with no knowledge scores
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A bottom-up stepwise knowledge-integration approach to large vocabulary continuous speech recognition using weighted finite state machines
No full textA bottom-up, stepwise, knowledge integration framework is
proposed to realize detection-based, large vocabulary continuous
speech recognition (LVCSR) with a weighted finite state
machine (WFSM). The WFSM framework offers a flexible
architecture for different types of knowledge network compositions,
each of them can be built and optimized independently.
Speech attribute detectors are used as an intermediate
block to obtain phoneme posterior probabilities over which a
phoneme recognition network is designed. Lexical access and
syntax knowledge integration over this phoneme network are
then performed to deliver the decoded sentences. Experimental
evidence illustrates that the proposed system outperforms
several hybrid HMM/ANN systems with different configurations
on the Wall Street Journal task while it is competitive
with conventional LVCSR technology
Toward a detector-based universal phone recognizer
No full textIn recent research, we have proposed a high-accuracy bottom-up detection-based paradigm for continuous phone speech recognition. The key component of our system was a bank of articulatory detectors each of which computes a score describing an activation level of the specified speech phonetic features that the current frame exhibits. In this work, we present our first attempt at designing a universal phone recognizer using the detection-based approach. We show that our technique is intrinsically language independent since reliable articulatory detectors can be designed for diverse languages, and robust detection can be performed across languages. Moreover, a universal set of detectors is designed by sharing the training material available for several diverse languages. We further demonstrate that our approach makes it possible to decode new target languages by neither retraining nor applying acoustic adaptation techniques. We report phone recognition performance that compares favorably with the best results known by the authors on the OGI Multi-language Telephone Speech corpus
Towards bottom-up continuous phone recognition
No full textWe present a novel approach to designing bottom-up automatic speech recognition (ASR) systems. The key component of the proposed approach is a bank of articulatory attribute detectors implemented using a set of feed-forward artificial neural networks (ANNs). Each detector computes a score describing an activation level of the specified speech attributes that the current frame exhibits. These cues are first combined by an event merger that provides some evidence about the presence of a higher level feature which is then verified by an evidence verifier to produce hypotheses at the phone or word level. We evaluate several configurations of our proposed system on a continuous phone recognition task. Experimental results on the TIMIT database show that the system achieves a phone error rate of 25% which is superior to results obtained with either hidden Markov model (HMM) or conditional random field (CRF) based recognizers. We believe the system's inherent flexibility and the ease of adding new detectors may provide further improvements
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Exploiting context-dependency and acoustic resolution of universal speech attribute models in spoken language recognition
No full textThis paper expands a previously proposed universal acoustic characterization approach to spoken language identification (LID) by studying different ways of modeling attributes to improve language recognition. The motivation is to describe any spoken language with a common set of fundamental units. Thus, a spoken utterance is first tokenized into a sequence of universal attributes. Then a vector space modeling approach delivers the final LID decision. Context-dependent attribute models are now used to better capture spectral and temporal characteristics. Also, an approach to expand the set of attributes to increase the acoustic resolution is studied. Our experiments show that the tokenization accuracy positively affects LID results by producing a 2.8% absolute improvement over our previous 30-second NIST 2003 performance. This result also compares favorably with the best results on the same task known by the authors when the tokenizers are trained on language-dependent OGI-TS data
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