1,387 research outputs found
The 2005 AMI system for the transcription of speech in meetings
In this paper we describe the 2005 AMI system for the transcription\ud
of speech in meetings used for participation in the 2005 NIST\ud
RT evaluations. The system was designed for participation in the speech\ud
to text part of the evaluations, in particular for transcription of speech\ud
recorded with multiple distant microphones and independent headset\ud
microphones. System performance was tested on both conference room\ud
and lecture style meetings. Although input sources are processed using\ud
different front-ends, the recognition process is based on a unified system\ud
architecture. The system operates in multiple passes and makes use\ud
of state of the art technologies such as discriminative training, vocal\ud
tract length normalisation, heteroscedastic linear discriminant analysis,\ud
speaker adaptation with maximum likelihood linear regression and minimum\ud
word error rate decoding. In this paper we describe the system performance\ud
on the official development and test sets for the NIST RT05s\ud
evaluations. The system was jointly developed in less than 10 months\ud
by a multi-site team and was shown to achieve very competitive performance
Optimal Transport for Domain Adaptation
Domain adaptation from one data space (or domain) to another is one of the
most challenging tasks of modern data analytics. If the adaptation is done
correctly, models built on a specific data space become more robust when
confronted to data depicting the same semantic concepts (the classes), but
observed by another observation system with its own specificities. Among the
many strategies proposed to adapt a domain to another, finding a common
representation has shown excellent properties: by finding a common
representation for both domains, a single classifier can be effective in both
and use labelled samples from the source domain to predict the unlabelled
samples of the target domain. In this paper, we propose a regularized
unsupervised optimal transportation model to perform the alignment of the
representations in the source and target domains. We learn a transportation
plan matching both PDFs, which constrains labelled samples in the source domain
to remain close during transport. This way, we exploit at the same time the few
labeled information in the source and the unlabelled distributions observed in
both domains. Experiments in toy and challenging real visual adaptation
examples show the interest of the method, that consistently outperforms state
of the art approaches
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN
Call Centers have huge amount of audio data which can be used for achieving
valuable business insights and transcription of phone calls is manually tedious
task. An effective Automated Speech Recognition system can accurately
transcribe these calls for easy search through call history for specific
context and content allowing automatic call monitoring, improving QoS through
keyword search and sentiment analysis. ASR for Call Center requires more
robustness as telephonic environment are generally noisy. Moreover, there are
many low-resourced languages that are on verge of extinction which can be
preserved with help of Automatic Speech Recognition Technology. Urdu is the
most widely spoken language in the world, with 231,295,440 worldwide
still remains a resource constrained language in ASR. Regional call-center
conversations operate in local language, with a mix of English numbers and
technical terms generally causing a "code-switching" problem. Hence, this paper
describes an implementation framework of a resource efficient Automatic Speech
Recognition/ Speech to Text System in a noisy call-center environment using
Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid
HMM-DNN approach allowed us to utilize the advantages of Neural Network with
less labelled data. Adding CNN with TDNN has shown to work better in noisy
environment due to CNN's additional frequency dimension which captures extra
information from noisy speech, thus improving accuracy. We collected data from
various open sources and labelled some of the unlabelled data after analysing
its general context and content from Urdu language as well as from commonly
used words from other languages, primarily English and were able to achieve WER
of 5.2% with noisy as well as clean environment in isolated words or numbers as
well as in continuous spontaneous speech.Comment: 32 pages, 19 figures, 2 tables, preprin
Interactive game for the training of portuguese vowels
Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200
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