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

Large vocabulary continuous speech recognition using linguistic features and constraints

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 111-123).Automatic speech recognition (ASR) is a process of applying constraints, as encoded in the computer system (the recognizer), to the speech signal until ambiguity is satisfactorily resolved to the extent that only one sequence of words is hypothesized. Such constraints fall naturally into two categories. One deals with the ordering of words (syntax) and organization of their meanings (semantics, pragmatics, etc). The other governs how speech signals are related to words, a process often termed as lexical access". This thesis studies the Huttenlocher-Zue lexical access model, its implementation in a modern probabilistic speech recognition framework and its application to continuous speech from an open vocabulary. The Huttenlocher-Zue model advocates a two-pass lexical access paradigm. In the first pass, the lexicon is effectively pruned using broad linguistic constraints. In the original Huttenlocher-Zue model, the authors had proposed six linguistic features motivated by the manner of pronunciation. The first pass classifies speech signals into a sequence of linguistic features, and only words that match this sequence - the cohort - are activated. The second pass performs a detailed acoustic phonetic analysis within the cohort to decide the identity of the word. This model differs from the lexical access model nowadays commonly employed in speech recognizers where detailed acoustic phonetic analysis is performed directly and lexical items are retrieved in one pass. The thesis first studies the implementation issues of the Huttenlocher-Zue model. A number of extensions to the original proposal are made to take advantage of the existing facilities of a probabilistic, graph-based recognition framework and, more importantly, to model the broad linguistic features in a data-driven approach. First, we analyze speech signals along the two diagonal dimensions of manner and place of articulation, rather than the manner dimension alone. Secondly, we adopt a set of feature-based landmarks optimized for data-driven modeling as the basic recognition units, and Gaussian mixture models are trained for these units. We explore information fusion techniques to integrate constraints from both the manner and place dimensions, as well as examining how to integrate constraints from the feature-based first pass with the second pass of detailed acoustic phonetic analysis. Our experiments on a large-vocabulary isolated word recognition task show that, while constraints from each individual feature dimension provide only limited help in this lexical access model, the utilization of both dimensions and information fusion techniques leads to significant performance gain over a one-pass phonetic system. The thesis then proposes to generalize the original Huttenlocher-Zue model, which limits itself to only isolated word tasks, to handle continuous speech. With continuous speech, the search space for both stages is infinite if all possible word sequences are allowed. We generalize the original cohort idea from the Huttenlocher-Zue proposal and use the bag of words of the N-best list of the first pass as cohorts for continuous speech. This approach transfers the constraints of broad linguistic features into a much reduced search space for the second stage. The thesis also studies how to recover from errors made by the first pass, which is not discussed in the original Huttenlocher- Zue proposal. In continuous speech recognition, a way of recovering from errors made in the first pass is vital to the performance of the over-all system. We find empirical evidence that such errors tend to occur around function words, possibly due to the lack of prominence, in meaning and henceforth in linguistic features, of such words. This thesis proposes an error-recovery mechanism based on empirical analysis on a development set for the two-pass lexical access model. Our experiments on a medium- sized, telephone-quality continuous speech recognition task achieve higher accuracy than a state-of-the-art one-pass baseline system. The thesis applies the generalized two-pass lexical access model to the challenge of recognizing continuous speech from an open vocabulary. Telephony information query systems often need to deal with a large list of words that are not observed in the training data, for example the city names in a weather information query system. The large portion of vocabulary unseen in the training data - the open vocabulary - poses a serious data-sparseness problem to both acoustic and language modeling. A two-pass lexical access model provides a solution by activating a small cohort within the open vocabulary in the first pass, thus significantly reducing the data- sparseness problem. Also, the broad linguistic constraints in the first pass generalize better to unseen data compared to finer, context-dependent acoustic phonetic models. This thesis also studies a data-driven analysis of acoustic similarities among open vocabulary items. The results are used for recovering possible errors in the first pass. This approach demonstrates an advantage over a two-pass approach based on specific semantic constraints. In summary, this thesis implements the original Huttenlocher-Zue two-pass lexical access model in a modern probabilistic speech recognition framework. This thesis also extends the original model to recognize continuous speech from an open vocabulary, with our two-stage model achieving a better performance than the baseline system. In the future, sub-lexical linguistic hierarchy constraints, such as syllables, can be introduced into this two-pass model to further improve the lexical access performance.by Min Tang.Ph.D

GREC: Multi-domain Speech Recognition for the Greek Language

Μία από τις κορυφαίες προκλήσεις στην Αυτόματη Αναγνώριση Ομιλίας είναι η ανάπτυξη ικανών συστημάτων που μπορούν να έχουν ισχυρή απόδοση μέσα από διαφορετικές συνθήκες ηχογράφησης. Στο παρόν έργο κατασκευάζουμε και αναλύουμε το GREC, μία μεγάλη πολυτομεακή συλλογή δεδομένων για αυτόματη αναγνώριση ομιλίας στην ελληνική γλώσσα. Το GREC αποτελείται από τρεις βάσεις δεδομένων στους θεματικούς τομείς των «εκπομπών ειδήσεων», «ομιλίας από δωρισμένες εγγραφές φωνής», «ηχητικών βιβλίων» και μιας νέας συλλογής δεδομένων στον τομέα των «πολιτικών ομιλιών». Για τη δημιουργία του τελευταίου, συγκεντρώνουμε δεδομένα ομιλίας από ηχογραφήσεις των επίσημων συνεδριάσεων της Βουλής των Ελλήνων, αποδίδοντας ένα σύνολο δεδομένων που αποτελείται από 120 ώρες ομιλίας πολιτικού περιεχομένου. Περιγράφουμε με λεπτομέρεια την καινούρια συλλογή δεδομένων, την προεπεξεργασία και την ευθυγράμμιση ομιλίας, τα οποία βασίζονται στο εργαλείο ανοιχτού λογισμικού Kaldi. Επιπλέον, αξιολογούμε την απόδοση των μοντέλων Gaussian Mixture (GMM) - Hidden Markov (HMM) και Deep Neural Network (DNN) - HMM όταν εφαρμόζονται σε δεδομένα από διαφορετικούς τομείς. Τέλος, προσθέτουμε τη δυνατότητα αυτόματης δεικτοδότησης ομιλητών στο Kaldi-gRPC-Server, ενός εργαλείου γραμμένο σε Python που βασίζεται στο PyKaldi και στο gRPC για βελτιωμένη ανάπτυξη μοντέλων αυτόματης αναγνώρισης ομιλίας.One of the leading challenges in Automatic Speech Recognition (ASR) is the development of robust systems that can perform well under multiple settings. In this work we construct and analyze GREC, a large, multi-domain corpus for automatic speech recognition for the Greek language. GREC is a collection of three available subcorpora over the domains of “news casts”, “crowd-sourced speech”, “audiobooks”, and a new corpus in the domain of “public speeches”. For the creation of the latter, HParl, we collect speech data from recordings of the official proceedings of the Hellenic Parliament, yielding, a dataset which consists of 120 hours of political speech segments. We describe our data collection, pre-processing and alignment setup, which are based on Kaldi toolkit. Furthermore, we perform extensive ablations on the recognition performance of Gaussian Mixture (GMM) - Hidden Markov (HMM) models and Deep Neural Network (DNN) - HMM models over the different domains. Finally, we integrate speaker diarization features to Kaldi-gRPC-Server, a modern, pythonic tool based on PyKaldi and gRPC for streamlined deployment of Kaldi based speech recognition

Ensembles for sequence learning

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement learning problems. In both types of problems, even after having observed a certain amount of data, we are often faced with uncertainty as to which hypothesis is correct among all the possible ones. However, in many methods for both supervised and for reinforcement learning problems this uncertainty is ignored, in the sense that there is a single solution selected out of the whole of the hypothesis space. Apart from the classical solution of analytical Bayesian formulations, ensemble methods offer an alternative approach to representing this uncertainty. This is done simply through maintaining a set of alternative hypotheses. The sequential supervised problem considered is that of automatic speech recognition using hidden Markov models. The application of ensemble methods to the problem represents a challenge in itself, since most such methods can not be readily adapted to sequential learning tasks. This thesis proposes a number of different approaches for applying ensemble methods to speech recognition and develops methods for effective training of phonetic mixtures with or without access to phonetic alignment data. Furthermore, the notion of expected loss is introduced for integrating probabilistic models with the boosting approach. In some cases substantial improvements over the baseline system are obtained. In reinforcement learning problems the goal is to act in such a way as to maximise future reward in a given environment. In such problems uncertainty becomes important since neither the environment nor the distribution of rewards that result from each action are known. This thesis presents novel algorithms for acting nearly optimally under uncertainty based on theoretical considerations. Some ensemble-based representations of uncertainty (including a fully Bayesian model) are developed and tested on a few simple tasks resulting in performance comparable with the state of the art. The thesis also draws some parallels between a proposed representation of uncertainty based on gradient-estimates and on"prioritised sweeping" and between the application of reinforcement learning to controlling an ensemble of classifiers and classical supervised ensemble learning methods

Search Space Pruning Based on Anticipated Path Recombination in LVCSR

In this paper we introduce a well-motivated abstract pruning criterion for LVCSR decoders based on the anticipated recombination of HMM state alignment paths. We show that several heuristical pruning methods common in dynamic network decoders are approximations of this pruning criterion. The abstract criterion is too complex to be applied directly in an efficient manner, so we derive approximations which can be applied efficiently. Our new pruning methods allow much more exhaustive pruning of the search space than previous methods. We show that the size of the search space can be reduced by up to 50 % at equal precision over the previous state of the art, and the RTF by 20%. The abstract pruning criterion can be considered a guide to derive effective pruning methods for any kind of time synchronous decoder. Index Terms: speech recognition, search, pruning 1