612 research outputs found
Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks
© 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] In the last years, Deep Bidirectional Recurrent Neural Networks (DBRNN) and DBRNN with Long Short-Term Memory cells (DBLSTM) have outperformed the most accurate classifiers for confidence estimation in automatic speech recognition. At the same time, we have recently shown that speaker adaptation of confidence measures using DBLSTM yields significant improvements over non-adapted confidence measures. In accordance with these two recent contributions to the state of the art in confidence estimation, this paper presents a comprehensive study of speaker-adapted confidence measures using DBRNN and DBLSTM models. Firstly, we present new empirical evidences of the superiority of RNN-based confidence classifiers evaluated over a large speech corpus consisting of the English LibriSpeech and the Spanish poliMedia tasks. Secondly, we show new results on speaker-adapted confidence measures considering a multi-task framework in which RNN-based confidence classifiers trained with LibriSpeech are adapted to speakers of the TED-LIUM corpus. These experiments confirm that speaker-adapted confidence measures outperform their non-adapted counterparts. Lastly, we describe an unsupervised adaptation method of the acoustic DBLSTM model based on confidence measures which results in better automatic speech recognition performance.This work was supported in part by the European Union's Horizon 2020 research and innovation programme under Grant 761758 (X5gon), in part by the Seventh Framework Programme (FP7/2007-2013) under Grant 287755 (transLectures), in part by the ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme under Grant 621030 (EMMA), and in part by the Spanish Government's TIN2015-68326-R (MINECO/FEDER) research project MORE.Del Agua Teba, MA.; Giménez Pastor, A.; Sanchis Navarro, JA.; Civera Saiz, J.; Juan, A. (2018). Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(7):1198-1206. https://doi.org/10.1109/TASLP.2018.2819900S1198120626
DNN adaptation by automatic quality estimation of ASR hypotheses
In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201
CONTRIBUTIONS TO EFFICIENT AUTOMATIC TRANSCRIPTION OF VIDEO LECTURES
Tesis por compendio[ES] Durante los últimos años, los repositorios multimedia en línea se han convertido
en fuentes clave de conocimiento gracias al auge de Internet, especialmente en
el área de la educación. Instituciones educativas de todo el mundo han dedicado
muchos recursos en la búsqueda de nuevos métodos de enseñanza, tanto para
mejorar la asimilación de nuevos conocimientos, como para poder llegar a una
audiencia más amplia. Como resultado, hoy en día disponemos de diferentes
repositorios con clases grabadas que siven como herramientas complementarias en
la enseñanza, o incluso pueden asentar una nueva base en la enseñanza a
distancia. Sin embargo, deben cumplir con una serie de requisitos para que la
experiencia sea totalmente satisfactoria y es aquí donde la transcripción de los
materiales juega un papel fundamental. La transcripción posibilita una búsqueda
precisa de los materiales en los que el alumno está interesado, se abre la
puerta a la traducción automática, a funciones de recomendación, a la
generación de resumenes de las charlas y además, el poder hacer
llegar el contenido a personas con discapacidades auditivas. No obstante, la
generación de estas transcripciones puede resultar muy costosa.
Con todo esto en mente, la presente tesis tiene como objetivo proporcionar
nuevas herramientas y técnicas que faciliten la transcripción de estos
repositorios. En particular, abordamos el desarrollo de un conjunto de herramientas
de reconocimiento de automático del habla, con énfasis en las técnicas de aprendizaje
profundo que contribuyen a proporcionar transcripciones precisas en casos de
estudio reales. Además, se presentan diferentes participaciones en competiciones
internacionales donde se demuestra la competitividad del software comparada con
otras soluciones. Por otra parte, en aras de mejorar los sistemas de
reconocimiento, se propone una nueva técnica de adaptación de estos sistemas al
interlocutor basada en el uso Medidas de Confianza. Esto además motivó el
desarrollo de técnicas para la mejora en la estimación de este tipo de medidas
por medio de Redes Neuronales Recurrentes.
Todas las contribuciones presentadas se han probado en diferentes repositorios
educativos. De hecho, el toolkit transLectures-UPV es parte de un conjunto de
herramientas que sirve para generar transcripciones de clases en diferentes
universidades e instituciones españolas y europeas.[CA] Durant els últims anys, els repositoris multimèdia en línia s'han convertit
en fonts clau de coneixement gràcies a l'expansió d'Internet, especialment en
l'àrea de l'educació. Institucions educatives de tot el món han dedicat
molts recursos en la recerca de nous mètodes d'ensenyament, tant per
millorar l'assimilació de nous coneixements, com per poder arribar a una
audiència més àmplia. Com a resultat, avui dia disposem de diferents
repositoris amb classes gravades que serveixen com a eines complementàries en
l'ensenyament, o fins i tot poden assentar una nova base a l'ensenyament a
distància. No obstant això, han de complir amb una sèrie de requisits perquè la
experiència siga totalment satisfactòria i és ací on la transcripció dels
materials juga un paper fonamental. La transcripció possibilita una recerca
precisa dels materials en els quals l'alumne està interessat, s'obri la
porta a la traducció automàtica, a funcions de recomanació, a la
generació de resums de les xerrades i el poder fer
arribar el contingut a persones amb discapacitats auditives. No obstant, la
generació d'aquestes transcripcions pot resultar molt costosa.
Amb això en ment, la present tesi té com a objectiu proporcionar noves
eines i tècniques que faciliten la transcripció d'aquests repositoris. En
particular, abordem el desenvolupament d'un conjunt d'eines de reconeixement
automàtic de la parla, amb èmfasi en les tècniques d'aprenentatge profund que
contribueixen a proporcionar transcripcions precises en casos d'estudi reals. A
més, es presenten diferents participacions en competicions internacionals on es
demostra la competitivitat del programari comparada amb altres solucions.
D'altra banda, per tal de millorar els sistemes de reconeixement, es proposa una
nova tècnica d'adaptació d'aquests sistemes a l'interlocutor basada en l'ús de
Mesures de Confiança. A més, això va motivar el desenvolupament de tècniques per
a la millora en l'estimació d'aquest tipus de mesures per mitjà de Xarxes
Neuronals Recurrents.
Totes les contribucions presentades s'han provat en diferents repositoris
educatius. De fet, el toolkit transLectures-UPV és part d'un conjunt d'eines
que serveix per generar transcripcions de classes en diferents universitats i
institucions espanyoles i europees.[EN] During the last years, on-line multimedia repositories have become key
knowledge assets thanks to the rise of Internet and especially in the area of
education. Educational institutions around the world have devoted big efforts
to explore different teaching methods, to improve the transmission of knowledge
and to reach a wider audience. As a result, online video lecture repositories
are now available and serve as complementary tools that can boost the learning
experience to better assimilate new concepts. In order to guarantee the success
of these repositories the transcription of each lecture plays a very important
role because it constitutes the first step towards the availability of many other
features. This transcription allows the searchability of learning materials,
enables the translation into another languages, provides recommendation
functions, gives the possibility to provide content summaries, guarantees
the access to people with hearing disabilities, etc. However, the
transcription of these videos is expensive in terms of time and human cost.
To this purpose, this thesis aims at providing new tools and techniques that
ease the transcription of these repositories. In particular, we address the
development of a complete Automatic Speech Recognition Toolkit with an special
focus on the Deep Learning techniques that contribute to provide accurate
transcriptions in real-world scenarios. This toolkit is tested against many
other in different international competitions showing comparable transcription
quality. Moreover, a new technique to improve the recognition accuracy has been
proposed which makes use of Confidence Measures, and constitutes the spark that
motivated the proposal of new Confidence Measures techniques that helped to
further improve the transcription quality. To this end, a new speaker-adapted
confidence measure approach was proposed for models based on Recurrent Neural
Networks.
The contributions proposed herein have been tested in real-life scenarios in
different educational repositories. In fact, the transLectures-UPV toolkit is
part of a set of tools for providing video lecture transcriptions in many
different Spanish and European universities and institutions.Agua Teba, MÁD. (2019). CONTRIBUTIONS TO EFFICIENT AUTOMATIC TRANSCRIPTION OF VIDEO LECTURES [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130198TESISCompendi
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems
Speaker adaptation techniques provide a powerful solution to customise
automatic speech recognition (ASR) systems for individual users. Practical
application of unsupervised model-based speaker adaptation techniques to data
intensive end-to-end ASR systems is hindered by the scarcity of speaker-level
data and performance sensitivity to transcription errors. To address these
issues, a set of compact and data efficient speaker-dependent (SD) parameter
representations are used to facilitate both speaker adaptive training and
test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR
systems. The sensitivity to supervision quality is reduced using a confidence
score-based selection of the less erroneous subset of speaker-level adaptation
data. Two lightweight confidence score estimation modules are proposed to
produce more reliable confidence scores. The data sparsity issue, which is
exacerbated by data selection, is addressed by modelling the SD parameter
uncertainty using Bayesian learning. Experiments on the benchmark 300-hour
Switchboard and the 233-hour AMI datasets suggest that the proposed confidence
score-based adaptation schemes consistently outperformed the baseline
speaker-independent (SI) Conformer model and conventional non-Bayesian, point
estimate-based adaptation using no speaker data selection. Similar consistent
performance improvements were retained after external Transformer and LSTM
language model rescoring. In particular, on the 300-hour Switchboard corpus,
statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute
(9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer
on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Similar WER
reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also
obtained on the AMI development and evaluation sets.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin
Fast and Accurate OOV Decoder on High-Level Features
This work proposes a novel approach to out-of-vocabulary (OOV) keyword search
(KWS) task. The proposed approach is based on using high-level features from an
automatic speech recognition (ASR) system, so called phoneme posterior based
(PPB) features, for decoding. These features are obtained by calculating
time-dependent phoneme posterior probabilities from word lattices, followed by
their smoothing. For the PPB features we developed a special novel very fast,
simple and efficient OOV decoder. Experimental results are presented on the
Georgian language from the IARPA Babel Program, which was the test language in
the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum
term weighted value (MTWV) metric and computational speed, for single ASR
systems, the proposed approach significantly outperforms the state-of-the-art
approach based on using in-vocabulary proxies for OOV keywords in the indexed
database. The comparison of the two OOV KWS approaches on the fusion results of
the nine different ASR systems demonstrates that the proposed OOV decoder
outperforms the proxy-based approach in terms of MTWV metric given the
comparable processing speed. Other important advantages of the OOV decoder
include extremely low memory consumption and simplicity of its implementation
and parameter optimization.Comment: Interspeech 2017, August 2017, Stockholm, Sweden. 201
Recommended from our members
Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks
Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks
Recently, there has been growth in providers of speech transcription services
enabling others to leverage technology they would not normally be able to use.
As a result, speech-enabled solutions have become commonplace. Their success
critically relies on the quality, accuracy, and reliability of the underlying
speech transcription systems. Those black box systems, however, offer limited
means for quality control as only word sequences are typically available. This
paper examines this limited resource scenario for confidence estimation, a
measure commonly used to assess transcription reliability. In particular, it
explores what other sources of word and sub-word level information available in
the transcription process could be used to improve confidence scores. To encode
all such information this paper extends lattice recurrent neural networks to
handle sub-words. Experimental results using the IARPA OpenKWS 2016 evaluation
system show that the use of additional information yields significant gains in
confidence estimation accuracy. The implementation for this model can be found
online.Comment: 5 pages, 8 figures, ICASSP submissio
Confidence-based Ensembles of End-to-End Speech Recognition Models
The number of end-to-end speech recognition models grows every year. These
models are often adapted to new domains or languages resulting in a
proliferation of expert systems that achieve great results on target data,
while generally showing inferior performance outside of their domain of
expertise. We explore combination of such experts via confidence-based
ensembles: ensembles of models where only the output of the most-confident
model is used. We assume that models' target data is not available except for a
small validation set. We demonstrate effectiveness of our approach with two
applications. First, we show that a confidence-based ensemble of 5 monolingual
models outperforms a system where model selection is performed via a dedicated
language identification block. Second, we demonstrate that it is possible to
combine base and adapted models to achieve strong results on both original and
target data. We validate all our results on multiple datasets and model
architectures.Comment: To appear in Proc. INTERSPEECH 2023, August 20-24, 2023, Dublin,
Irelan
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