Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Statistical Models and Search Algorithms for Machine Translation

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Low Resource Efficient Speech Retrieval

Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency. This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding. Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR

Multi-view Representation Learning for Unifying Languages, Knowledge and Vision

The growth of content on the web has raised various challenges, yet also provided numerous opportunities. Content exists in varied forms such as text appearing in different languages, entity-relationship graph represented as structured knowledge and as a visual embodiment like images/videos. They are often referred to as modalities. In many instances, the different amalgamation of modalities co-exists to complement each other or to provide consensus. Thus making the content either heterogeneous or homogeneous. Having an additional point of view for each instance in the content is beneficial for data-driven learning and intelligent content processing. However, despite having availability of such content. Most advancements made in data-driven learning (i.e., machine learning) is by solving tasks separately for the single modality. The similar endeavor was not shown for the challenges which required input either from all or subset of them. In this dissertation, we develop models and techniques that can leverage multiple views of heterogeneous or homogeneous content and build a shared representation for aiding several applications which require a combination of modalities mentioned above. In particular, we aim to address applications such as content-based search, categorization, and generation by providing several novel contributions. First, we develop models for heterogeneous content by jointly modeling diverse representations emerging from two views depicting text and image by learning their correlation. To be specific, modeling such correlation is helpful to retrieve cross-modal content. Second, we replace the heterogeneous content with homogeneous to learn a common space representation for content categorization across languages. Furthermore, we develop models that take input from both homogeneous and heterogeneous content to facilitate the construction of common space representation from more than two views. Specifically, representation is used to generate one view from another. Lastly, we describe a model that can handle missing views, and demonstrate that the model can generate missing views by utilizing external knowledge. We argue that techniques the models leverage internally provide many practical benefits and lot of immediate value applications. From the modeling perspective, our contributed model design in this thesis can be summarized under the phrase Multi-view Representation Learning( MVRL ). These models are variations and extensions of shallow statistical and deep neural networks approaches that can jointly optimize and exploit all views of the input content arising from different independent representations. We show that our models advance state of the art, but not limited to tasks such as cross-modal retrieval, cross-language text classification, image-caption generation in multiple languages and caption generation for images containing unseen visual object categories

Supervised and unsupervised methods for learning representations of linguistic units

Word representations, also called word embeddings, are generic representations, often high-dimensional vectors. They map the discrete space of words into a continuous vector space, which allows us to handle rare or even unseen events, e.g. by considering the nearest neighbors. Many Natural Language Processing tasks can be improved by word representations if we extend the task specific training data by the general knowledge incorporated in the word representations. The first publication investigates a supervised, graph-based method to create word representations. This method leads to a graph-theoretic similarity measure, CoSimRank, with equivalent formalizations that show CoSimRank’s close relationship to Personalized Page-Rank and SimRank. The new formalization is efficient because it can use the graph-based word representation to compute a single node similarity without having to compute the similarities of the entire graph. We also show how we can take advantage of fast matrix multiplication algorithms. In the second publication, we use existing unsupervised methods for word representation learning and combine these with semantic resources by learning representations for non-word objects like synsets and entities. We also investigate improved word representations which incorporate the semantic information from the resource. The method is flexible in that it can take any word representations as input and does not need an additional training corpus. A sparse tensor formalization guarantees efficiency and parallelizability. In the third publication, we introduce a method that learns an orthogonal transformation of the word representation space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We use ultradense representations for a Lexicon Creation task in which words are annotated with three types of lexical information – sentiment, concreteness and frequency. The final publication introduces a new calculus for the interpretable ultradense subspaces, including polarity, concreteness, frequency and part-of-speech (POS). The calculus supports operations like “−1 × hate = love” and “give me a neutral word for greasy” (i.e., oleaginous) and extends existing analogy computations like “king − man + woman = queen”.Wortrepräsentationen, sogenannte Word Embeddings, sind generische Repräsentationen, meist hochdimensionale Vektoren. Sie bilden den diskreten Raum der Wörter in einen stetigen Vektorraum ab und erlauben uns, seltene oder ungesehene Ereignisse zu behandeln -- zum Beispiel durch die Betrachtung der nächsten Nachbarn. Viele Probleme der Computerlinguistik können durch Wortrepräsentationen gelöst werden, indem wir spezifische Trainingsdaten um die allgemeinen Informationen erweitern, welche in den Wortrepräsentationen enthalten sind. In der ersten Publikation untersuchen wir überwachte, graphenbasierte Methodenn um Wortrepräsentationen zu erzeugen. Diese Methoden führen zu einem graphenbasierten Ähnlichkeitsmaß, CoSimRank, für welches zwei äquivalente Formulierungen existieren, die sowohl die enge Beziehung zum personalisierten PageRank als auch zum SimRank zeigen. Die neue Formulierung kann einzelne Knotenähnlichkeiten effektiv berechnen, da graphenbasierte Wortrepräsentationen benutzt werden können. In der zweiten Publikation verwenden wir existierende Wortrepräsentationen und kombinieren diese mit semantischen Ressourcen, indem wir Repräsentationen für Objekte lernen, welche keine Wörter sind, wie zum Beispiel Synsets und Entitäten. Die Flexibilität unserer Methode zeichnet sich dadurch aus, dass wir beliebige Wortrepräsentationen als Eingabe verwenden können und keinen zusätzlichen Trainingskorpus benötigen. In der dritten Publikation stellen wir eine Methode vor, die eine Orthogonaltransformation des Vektorraums der Wortrepräsentationen lernt. Diese Transformation fokussiert relevante Informationen in einen ultra-kompakten Untervektorraum. Wir benutzen die ultra-kompakten Repräsentationen zur Erstellung von Wörterbüchern mit drei verschiedene Angaben -- Stimmung, Konkretheit und Häufigkeit. Die letzte Publikation präsentiert eine neue Rechenmethode für die interpretierbaren ultra-kompakten Untervektorräume -- Stimmung, Konkretheit, Häufigkeit und Wortart. Diese Rechenmethode beinhaltet Operationen wie ”−1 × Hass = Liebe” und ”neutrales Wort für Winkeladvokat” (d.h., Anwalt) und erweitert existierende Rechenmethoden, wie ”Onkel − Mann + Frau = Tante”

Accent intelligibility across native and non-native accent pairings:investigating links with electrophysiological measures of word recognition

The intelligibility of accented speech in noise depends on the interaction of the accents of the talker and the listener. However, it is not yet clear how this influence arises. Accent familiarity is commonly proposed to be a major contributor to accent intelligibility, but recent evidence suggests that the similarity between talker and listener accents may also be able to account for accent intelligibility across talker-listener pairings. In addition, differences in accent intelligibility are also often only found in the presence of other adverse conditions, so it is not clear if the talker-listener pairing also influences speech processing in quiet conditions. This research had two main aims; to further investigate the relationship between accent similarity and intelligibility, and to use online EEG methods to explore the possible presence of talker-listener pairing related differences on speech perception in quiet conditions. English and Spanish listeners listened to Standard Southern British English (SSBE), Glaswegian English (GE) and Spanish-accented English (SpE) in a speech-in-noise recognition task, and also completed an event-related potential (ERP) task to elicit the PMN and N400 responses. Accent similarity was measured using the ACCDIST metric. Results showed the same (or extremely similar) patterns in accent intelligibility and accent similarity for both listener groups, giving further support to the hypothesis that accent similarity can contribute to the level of intelligibility of an accent within a talker-listener pairing. ERP data also suggest that speech processing in quiet is influenced by the talker-listener pairing. The PMN, which relates to phonological processing, seems particularly dependent on a match between talker and listener accent, but the more semantic N400 showed some flexibility in the ability to process accented speech

Accent Intelligibility Differences in Noise Across Native and Nonnative Accents: Effects of Talker-Listener Pairing at Acoustic-Phonetic and Lexical Levels

PURPOSE: The intelligibility of an accent strongly depends on the specific talker–listener pairing. To explore the causes of this phenomenon, we investigated the relationship between acoustic–phonetic similarity and accent intelligibility across native (1st language) and nonnative (2nd language) talker–listener pairings. We also used online measures to observe processing differences in quiet. METHOD: English (n = 16) and Spanish (n = 16) listeners heard Standard Southern British English, Glaswegian English, and Spanish-accented English in a speech recognition task (in quiet and noise) and an electroencephalogram task (quiet only) designed to assess phonological and lexical processing. Stimuli were drawn from the nonnative speech recognition sentences (Stringer & Iverson, 2019). The acoustic–phonetic similarity between listeners' accents and the 3 accents was calculated using the ACCDIST metric (Huckvale, 2004, 2007). RESULTS: Talker–listener pairing had a clear influence on accent intelligibility. This was linked to the phonetic similarity of the talkers and the listeners, but similarity could not account for all findings. The influence of talker–listener pairing on lexical processing was less clear; the N400 effect was mostly robust to accent mismatches, with some relationship to intelligibility. CONCLUSION: These findings suggest that the influence of talker–listener pairing on intelligibility may be partly attributable to accent similarity in addition to accent familiarity. Online measures also show that differences in talker–listener accents can disrupt processing in quiet even where accents are highly intelligible