20 research outputs found
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
Semi-Supervised Acoustic Model Training by Discriminative Data Selection from Multiple ASR Systems' Hypotheses
While the performance of ASR systems depends on the size of the training data, it is very costly to prepare accurate and faithful transcripts. In this paper, we investigate a semisupervised training scheme, which takes the advantage of huge quantities of unlabeled video lecture archive, particularly for the deep neural network (DNN) acoustic model. In the proposed method, we obtain ASR hypotheses by complementary GMM-and DNN-based ASR systems. Then, a set of CRF-based classifiers is trained to select the correct hypotheses and verify the selected data. The proposed hypothesis combination shows higher quality compared with the conventional system combination method (ROVER). Moreover, compared with the conventional data selection based on confidence measure score, our method is demonstrated more effective for filtering usable data. Significant improvement in the ASR accuracy is achieved over the baseline system and in comparison with the models trained with the conventional system combination and data selection methods
Speech recognition experiments with audiobooks
Under real-life conditions several factors may be present that make the automatic recognition of speech difficult. The most obvious examples are background noise, peculiarities of the speaker's voice, sloppy articulation and strong emotional load. These all pose difficult problems for robust speech recognition, but it is not exactly clear how much each contributes to the difficulty of the task. In this paper we examine the abilities of our best recognition technologies under near-ideal conditions. The optimal conditions will be simulated by working with the sound material of an audiobook, in which most of the disturbing factors mentioned above are absent. Firstly pure phone recognition experiments will be performed, where neural net-based technologies will also be tried as well as the conventional Hidden Markov Models. Then we move on to large vocabulary recognition, where morphbased language models are applied to improve the performance of the standard word-based technology. The tests clearly justify our assertion that audiobooks pose a much easier recognition task than real-life databases. In both types of tasks we report the lowest error rates we have achieved so far in Hungarian continuous speech recognition
Language Modeling for limited-data domains
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 99-109).With the increasing focus of speech recognition and natural language processing applications on domains with limited amount of in-domain training data, enhanced system performance often relies on approaches involving model adaptation and combination. In such domains, language models are often constructed by interpolating component models trained from partially matched corpora. Instead of simple linear interpolation, we introduce a generalized linear interpolation technique that computes context-dependent mixture weights from features that correlate with the component confidence and relevance for each n-gram context. Since the n-grams from partially matched corpora may not be of equal relevance to the target domain, we propose an n-gram weighting scheme to adjust the component n-gram probabilities based on features derived from readily available corpus segmentation and metadata to de-emphasize out-of-domain n-grams. In scenarios without any matched data for a development set, we examine unsupervised and active learning techniques for tuning the interpolation and weighting parameters. Results on a lecture transcription task using the proposed generalized linear interpolation and n-gram weighting techniques yield up to a 1.4% absolute word error rate reduction over a linearly interpolated baseline language model. As more sophisticated models are only as useful as they are practical, we developed the MIT Language Modeling (MITLM) toolkit, designed for efficient iterative parameter optimization, and released it to the research community.(cont.) With a compact vector-based n-gram data structure and optimized algorithm implementations, the toolkit not only improves the running time of common tasks by up to 40x, but also enables the efficient parameter tuning for language modeling techniques that were previously deemed impractical.by Bo-June (Paul) Hsu.Ph.D
A System for Simultaneous Translation of Lectures and Speeches
This thesis realizes the first existing automatic system for simultaneous speech-to-speech translation. The focus of this system is the automatic translation of (technical oriented) lectures and speeches from English to Spanish, but the different aspects described in this thesis will also be helpful for developing simultaneous translation systems for other domains or languages
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
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
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Ensemble generation and compression for speech recognition
For many tasks in machine learning, performance gains can often be obtained by combining together an ensemble of multiple systems. In Automatic Speech Recognition (ASR), a range of approaches can be used to combine an ensemble when performing recognition. However, many of these have computational costs that scale linearly with the ensemble size. One method to address this is teacher-student learning, which compresses the ensemble into a single student. The student is trained to emulate the combined ensemble, and only the student needs to be used when performing recognition. This thesis investigates both methods for ensemble generation and methods for ensemble compression.
The first contribution of this thesis is to explore approaches of generating multiple systems for an ensemble. The combined ensemble performance depends on both the accuracy of the individual members of the ensemble, as well as the diversity between their behaviours. The structured nature of speech allows for many ways that systems can be made different from each other. The experiments suggest that significant combination gains can be obtained by combining systems with different acoustic models, sets of state clusters, and sets of sub-word units. When performing recognition, these ensembles can be combined at the hypothesis and frame levels. However, these combination methods can be computationally expensive, as data is processed by multiple systems.
This thesis also considers approaches to compress an ensemble, and reduce the computational cost when performing recognition. Teacher-student learning is one such method. In standard teacher-student learning, information about the per-frame state cluster posteriors is propagated from the teacher ensemble to the student, to train the student to emulate the ensemble. However, this has two limitations. First, it requires that the teachers and student all use the same set of state clusters. This limits the allowed forms of diversities that the ensemble can have. Second, ASR is a sequence modelling task, and the frame-level posteriors that are propagated may not effectively convey all information about the sequence-level behaviours of the teachers. This thesis addresses both of these limitations.
The second contribution of this thesis is to address the first limitation, and allow for different sets of state clusters between systems. The proposed method maps the state cluster posteriors from the teachers' sets of state clusters to that of the student. The map is derived by considering a distance measure between posteriors of unclustered logical context-dependent states, instead of the usual state cluster. The experiments suggest that this proposed method can allow a student to effectively learn from an ensemble that has a diversity of state cluster sets. However, the experiments also suggest that the student may need to have a large set of state clusters to effectively emulate this ensemble. This thesis proposes to use a student with a multi-task topology, with an output layer for each of the different sets of state clusters. This can capture the phonetic resolution of having multiple sets of state clusters, while having fewer parameters than a student with a single large output layer.
The third contribution of this thesis is to address the second limitation of standard teacher-student learning, that only frame-level information is propagated to emulate the ensemble behaviour for the sequence modelling ASR task. This thesis proposes to generalise teacher-student learning to the sequence level, and propagate sequence posterior information. The proposed methods can also allow for many forms of ensemble diversities. The experiments suggest that by using these sequence-level methods, a student can learn to emulate the ensemble better. Recently, the lattice-free method has been proposed to train a system directly toward a sequence discriminative criterion. Ensembles of these systems can exhibit highly diverse behaviours, because the systems are not biased toward any cross-entropy forced alignments. It is difficult to apply standard frame-level teacher-student learning with these lattice-free systems, as they are often not designed to produce state cluster posteriors. Sequence-level teacher-student learning operates directly on the sequence posteriors, and can therefore be used directly with these lattice-free systems.
The proposals in this thesis are assessed on four ASR tasks. These are the augmented multi-party interaction meeting transcription, IARPA Babel Tok Pisin conversational telephone speech, English broadcast news, and multi-genre broadcast tasks. These datasets provide a variety of quantities of training data, recording environments, and speaking styles