279 research outputs found
UCSY-SC1: A Myanmar speech corpus for automatic speech recognition
This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
We propose to model the acoustic space of deep neural network (DNN)
class-conditional posterior probabilities as a union of low-dimensional
subspaces. To that end, the training posteriors are used for dictionary
learning and sparse coding. Sparse representation of the test posteriors using
this dictionary enables projection to the space of training data. Relying on
the fact that the intrinsic dimensions of the posterior subspaces are indeed
very small and the matrix of all posteriors belonging to a class has a very low
rank, we demonstrate how low-dimensional structures enable further enhancement
of the posteriors and rectify the spurious errors due to mismatch conditions.
The enhanced acoustic modeling method leads to improvements in continuous
speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in
both clean and noisy conditions, where upto 15.4% relative reduction in word
error rate (WER) is achieved
Out-of-vocabulary spoken term detection
Spoken term detection (STD) is a fundamental task for multimedia information
retrieval. A major challenge faced by an STD system is the serious performance reduction
when detecting out-of-vocabulary (OOV) terms. The difficulties arise not only
from the absence of pronunciations for such terms in the system dictionaries, but from
intrinsic uncertainty in pronunciations, significant diversity in term properties and a
high degree of weakness in acoustic and language modelling.
To tackle the OOV issue, we first applied the joint-multigram model to predict pronunciations
for OOV terms in a stochastic way. Based on this, we propose a stochastic
pronunciation model that considers all possible pronunciations for OOV terms so that
the high pronunciation uncertainty is compensated for.
Furthermore, to deal with the diversity in term properties, we propose a termdependent
discriminative decision strategy, which employs discriminative models to
integrate multiple informative factors and confidence measures into a classification
probability, which gives rise to minimum decision cost.
In addition, to address the weakness in acoustic and language modelling, we propose
a direct posterior confidence measure which replaces the generative models with
a discriminative model, such as a multi-layer perceptron (MLP), to obtain a robust
confidence for OOV term detection.
With these novel techniques, the STD performance on OOV terms was improved
substantially and significantly in our experiments set on meeting speech data
Automatic Speech Recognition for Northern SĂĄmi with comparison to other Uralic Languages
Speech technology applications for major languages are becoming widely available, but for many other languages there is no commercial interest in developing speech technology. As the lack of technology and applications will threaten the existence of these languages, it is important to study how to create speech recognizers with minimal effort and low resources.
As a test case, we have developed a Large Vocabulary Continuous Speech Recognizer for Northern SĂĄmi, an Finno-Ugric language that has little resources for speech technology available. Using only limited audio data, 2.5 hours, and the Northern SĂĄmi Wikipedia for the language model we achieved 7.6% Letter Error Rate (LER). With a language model based on a higher quality language corpus we achieved 4.2% LER. To put this in perspective we also trained systems in other, better-resourced, Finno-Ugric languages (Finnish and Estonian) with the same amount of data and compared those to state-of-the-art systems in those languages.Peer reviewe
Evolutionary discriminative confidence estimation for spoken term detection
The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0913-zSpoken term detection (STD) is the task of searching for occurrences
of spoken terms in audio archives. It relies on robust confidence estimation
to make a hit/false alarm (FA) decision. In order to optimize the decision
in terms of the STD evaluation metric, the confidence has to be discriminative.
Multi-layer perceptrons (MLPs) and support vector machines (SVMs) exhibit
good performance in producing discriminative confidence; however they are
severely limited by the continuous objective functions, and are therefore less
capable of dealing with complex decision tasks. This leads to a substantial
performance reduction when measuring detection of out-of-vocabulary (OOV)
terms, where the high diversity in term properties usually leads to a complicated
decision boundary.
In this paper we present a new discriminative confidence estimation approach
based on evolutionary discriminant analysis (EDA). Unlike MLPs and
SVMs, EDA uses the classification error as its objective function, resulting
in a model optimized towards the evaluation metric. In addition, EDA combines
heterogeneous projection functions and classification strategies in decision
making, leading to a highly flexible classifier that is capable of dealing
with complex decision tasks. Finally, the evolutionary strategy of EDA reduces the risk of local minima. We tested the EDA-based confidence with a
state-of-the-art phoneme-based STD system on an English meeting domain
corpus, which employs a phoneme speech recognition system to produce lattices
within which the phoneme sequences corresponding to the enquiry terms
are searched. The test corpora comprise 11 hours of speech data recorded with
individual head-mounted microphones from 30 meetings carried out at several
institutes including ICSI; NIST; ISL; LDC; the Virginia Polytechnic Institute
and State University; and the University of Edinburgh. The experimental results
demonstrate that EDA considerably outperforms MLPs and SVMs on
both classification and confidence measurement in STD, and the advantage
is found to be more significant on OOV terms than on in-vocabulary (INV)
terms. In terms of classification performance, EDA achieved an equal error
rate (EER) of 11% on OOV terms, compared to 34% and 31% with MLPs and
SVMs respectively; for INV terms, an EER of 15% was obtained with EDA
compared to 17% obtained with MLPs and SVMs. In terms of STD performance
for OOV terms, EDA presented a significant relative improvement of
1.4% and 2.5% in terms of average term-weighted value (ATWV) over MLPs
and SVMs respectively.This work was partially supported by the French Ministry of Industry
(Innovative Web call) under contract 09.2.93.0966, âCollaborative Annotation for Video
Accessibilityâ (ACAV) and by âThe Adaptable Ambient Living Assistantâ (ALIAS) project
funded through the joint national Ambient Assisted Living (AAL) programme
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