1,082 research outputs found
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model
Multilingual models for Automatic Speech Recognition (ASR) are attractive as
they have been shown to benefit from more training data, and better lend
themselves to adaptation to under-resourced languages. However, initialisation
from monolingual context-dependent models leads to an explosion of
context-dependent states. Connectionist Temporal Classification (CTC) is a
potential solution to this as it performs well with monophone labels.
We investigate multilingual CTC in the context of adaptation and
regularisation techniques that have been shown to be beneficial in more
conventional contexts. The multilingual model is trained to model a universal
International Phonetic Alphabet (IPA)-based phone set using the CTC loss
function. Learning Hidden Unit Contribution (LHUC) is investigated to perform
language adaptive training. In addition, dropout during cross-lingual
adaptation is also studied and tested in order to mitigate the overfitting
problem.
Experiments show that the performance of the universal phoneme-based CTC
system can be improved by applying LHUC and it is extensible to new phonemes
during cross-lingual adaptation. Updating all the parameters shows consistent
improvement on limited data. Applying dropout during adaptation can further
improve the system and achieve competitive performance with Deep Neural Network
/ Hidden Markov Model (DNN/HMM) systems on limited data
A spoken document retrieval application in the oral history domain
The application of automatic speech recognition in the broadcast news domain is well studied. Recognition performance is generally high and accordingly, spoken document retrieval can successfully be applied in this domain, as demonstrated by a number of commercial systems. In other domains, a similar recognition performance is hard to obtain, or even far out of reach, for example due to lack of suitable training material. This is a serious impediment for the successful application of spoken document retrieval techniques for other data then news. This paper outlines our first steps towards a retrieval system that can automatically be adapted to new domains. We discuss our experience with a recently implemented spoken document retrieval application attached to a web-portal that aims at the disclosure of a multimedia data collection in the oral history domain. The paper illustrates that simply deploying an off-theshelf\ud
broadcast news system in this task domain will produce error rates that are too high to be useful for retrieval tasks. By applying adaptation techniques on the acoustic level and language model level, system performance can be improved considerably, but additional research on unsupervised adaptation and search interfaces is required to create an adequate search environment based on speech transcripts
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Probabilistic Models of Short and Long Distance Word Dependencies in Running Text
This article describes two complementary models that represent dependencies between words in loca/ and non-local contexts. The type of local dependencies considered are sequences of part of speech categories for words. The non-local context of word dependency considered here is that of word recurrence, which is typical in a text. Both are models of phenomena that are to a reasonable extent domain independent, and thus are useful for doing prediction in systems using large vocabularies. Modeling Part of Speech Sequences A common method for modeling local word dependencies is by means of second order Markov models (also known as trigram models). In such a model the context for predicting word wi at position i in a text consists of the two words wi_l, wi-2 that precede it. The model is built from conditional probabilities: P(wi I wi_l, wi-2). The parameters of a part of speech (POS) model are of the form: P(wi [ Ci) x P(Ci [ Ci-1, Ci-2)
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
Automatic Speech Recognition for Speech Assessment of Persian Preschool Children
Preschool evaluation is crucial because it gives teachers and parents
influential knowledge about children's growth and development. The COVID-19
pandemic has highlighted the necessity of online assessment for preschool
children. One of the areas that should be tested is their ability to speak.
Employing an Automatic Speech Recognition(ASR) system is useless since they are
pre-trained on voices that are different from children's voices in terms of
frequency and amplitude. We constructed an ASR for our cognitive test system to
solve this issue using the Wav2Vec 2.0 model with a new pre-training objective
called Random Frequency Pitch(RFP). In addition, we used our new dataset to
fine-tune our model for Meaningless Words(MW) and Rapid Automatic Naming(RAN)
tests. Our new approach reaches a Word Error Rate(WER) of 6.45 on the Persian
section of the CommonVoice dataset. Furthermore, our novel methodology produces
positive outcomes in zero- and few-shot scenarios.Comment: 8 pages, 5 figures, 4 tables, 1 algorith
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