Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints

We propose a first step toward multilingual end-to-end automatic speech recognition (ASR) by integrating knowledge about speech articulators. The key idea is to leverage a rich set of fundamental units that can be defined "universally" across all spoken languages, referred to as speech attributes, namely manner and place of articulation. Specifically, several deterministic attribute-to-phoneme mapping matrices are constructed based on the predefined set of universal attribute inventory, which projects the knowledge-rich articulatory attribute logits, into output phoneme logits. The mapping puts knowledge-based constraints to limit inconsistency with acoustic-phonetic evidence in the integrated prediction. Combined with phoneme recognition, our phone recognizer is able to infer from both attribute and phoneme information. The proposed joint multilingual model is evaluated through phoneme recognition. In multilingual experiments over 6 languages on benchmark datasets LibriSpeech and CommonVoice, we find that our proposed solution outperforms conventional multilingual approaches with a relative improvement of 6.85% on average, and it also demonstrates a much better performance compared to monolingual model. Further analysis conclusively demonstrates that the proposed solution eliminates phoneme predictions that are inconsistent with attributes

ATLAS: A flexible and extensible architecture for linguistic annotation

We describe a formal model for annotating linguistic artifacts, from which we derive an application programming interface (API) to a suite of tools for manipulating these annotations. The abstract logical model provides for a range of storage formats and promotes the reuse of tools that interact through this API. We focus first on ``Annotation Graphs,'' a graph model for annotations on linear signals (such as text and speech) indexed by intervals, for which efficient database storage and querying techniques are applicable. We note how a wide range of existing annotated corpora can be mapped to this annotation graph model. This model is then generalized to encompass a wider variety of linguistic ``signals,'' including both naturally occuring phenomena (as recorded in images, video, multi-modal interactions, etc.), as well as the derived resources that are increasingly important to the engineering of natural language processing systems (such as word lists, dictionaries, aligned bilingual corpora, etc.). We conclude with a review of the current efforts towards implementing key pieces of this architecture.Comment: 8 pages, 9 figure

Improved language identification using deep bottleneck network

Effective representation plays an important role in automatic spoken language identification (LID). Recently, several representations that employ a pre-trained deep neural network (DNN) as the front-end feature extractor, have achieved state-of-the-art performance. However the performance is still far from satisfactory for dialect and short-duration utterance identification tasks, due to the deficiency of existing representations. To address this issue, this paper proposes the improved representations to exploit the information extracted from different layers of the DNN structure. This is conceptually motivated by regarding the DNN as a bridge between low-level acoustic input and high-level phonetic output features. Specifically, we employ deep bottleneck network (DBN), a DNN with an internal bottleneck layer acting as a feature extractor. We extract representations from two layers of this single network, i.e. DBN-TopLayer and DBN-MidLayer. Evaluations on the NIST LRE2009 dataset, as well as the more specific dialect recognition task, show that each representation can achieve an incremental performance gain. Furthermore, a simple fusion of the representations is shown to exceed current state-of-the-art performance

Una estrategia de procesamiento automático del habla basada en la detección de atributos

State-of-the-art automatic speech and speaker recognition systems are often built with a pattern matching framework that has proven to achieve low recognition error rates for a variety of resource-rich tasks when the volume of speech and text examples to build statistical acoustic and language models is plentiful, and the speaker, acoustics and language conditions follow a rigid protocol. However, because of the “blackbox” top-down knowledge integration approach, such systems cannot easily leverage a rich set of knowledge sources already available in the literature on speech, acoustics and languages. In this paper, we present a bottom-up approach to knowledge integration, called automatic speech attribute transcription (ASAT), which is intended to be “knowledge-rich”, so that new and existing knowledge sources can be verified and integrated into current spoken language systems to improve recognition accuracy and system robustness. Since the ASAT framework offers a “divide-and-conquer” strategy and a “plug-andplay” game plan, it will facilitate a cooperative speech processing community that every researcher can contribute to, with a view to improving speech processing capabilities which are currently not easily accessible to researchers in the speech science community.Los sistemas más novedosos de reconocimiento automático de habla y de locutor suelen basarse en un sistema de coincidencia de patrones. Gracias a este modo de trabajo, se han obtenido unos bajos índices de error de reconocimiento para una variedad de tareas ricas en recursos, cuando se aporta una cantidad abundante de ejemplos de habla y texto para el entrenamiento estadístico de los modelos acústicos y de lenguaje, y siempre que el locutor y las condiciones acústicas y lingüísticas sigan un protocolo estricto. Sin embargo, debido a su aplicación de un proceso ciego de integración del conocimiento de arriba a abajo, dichos sistemas no pueden aprovechar fácilmente toda una serie de conocimientos ya disponibles en la literatura sobre el habla, la acústica y las lenguas. En este artículo presentamos una aproximación de abajo a arriba a la integración del conocimiento, llamada transcripción automática de atributos del habla (conocida en inglés como automatic speech attribute transcription, ASAT). Dicho enfoque pretende ser “rico en conocimiento”, con el fin de poder verificar las fuentes de conocimiento, tanto nuevas como ya existentes, e integrarlas en los actuales sistemas de lengua hablada para mejorar la precisión del reconocimiento y la robustez del sistema. Dado que ASAT ofrece una estrategia de tipo “divide y vencerás” y un plan de juego de “instalación y uso inmediato” (en inglés, plugand-play), esto facilitará una comunidad cooperativa de procesamiento del habla a la que todo investigador pueda contribuir con vistas a mejorar la capacidad de procesamiento del habla, que en la actualidad no es fácilmente accesible a los investigadores de la comunidad de las ciencias del habla

Organizational and formational structures of networks in the mental lexicon: A State-of-the-art through systematic review

This state-of-the-art presents a systematic exploration on the use of network patterns in global research efforts to understand, organize and represent the mental lexicon. Results have shown an increase over recent years in the usage of complex, small-world and scale-free network patterns within the literature.With the increasing complexity of network patterns, we see more potential in the inter-disciplinary exploration of the mental lexicon through universal and mathematically-describable, behavioral patterns in small-world and scale-free networks. A systematic review of 36 items of methodologically-selected literature serve as a means to explore how the greater literary body understands network structures within the mental lexicon. Network-based approaches are discriminated between three contrasting varieties. These include: 'simple networks', characterized by arbitrarily organized graph patterns of metaphorical importance; 'connectionist networks', a broad category of networks which explore the structural features of a system through the analysis of emergent properties; and lastly 'complex networks', distinguished as small-world, scale-free networks which follow a strict and mathematically-describable structure in agreement with the Barabási-Albert model. Each network approach is explored in terms of their discernible di erences which relate to their parameters and affect their implications. A final evaluation of observed patterns within the selected literature is o ered, as well as an elaboration on the sense of trajectory beheld in the research in order to offer insight and orientation for future research

“And Jesus began to teach”: A Transitivity Analysis of The Parable of the Sower

Religious discourse offers significant value for linguistic analysis. Functional analysis of religious texts provides a lexico-semantic approach to interpreting these discourses, though it remains underexplored in the literature. To fill this gap, the study undertook a functional analysis of the Parable of the Sower within the framework of transitivity. A mixed-method approach was employed. The text was taken from the Modern English Version (MEV) (Mark 4:1-20). The process types and their participants and the worldview of the parable were analysed within the framework. The material process type dominated the text, occurring 42 times. This was followed by the relational, mental, verbal and behavioural process types. The actor participant dominated the data. The text is highly action-oriented with the world of the text concerned with happenings, movements and tangible actions. The worldview of the parable could be described as: the nature of Christians upon receiving the word and instructions towards bearing fruits. Enthusiasm, materialism, double-minded and being stressed characterize the nature of Christians upon receiving the word while commitment, focus, steadfastness and faithfulness represent the instructions towards seed bearing. The textual and interpersonal metafunctions of language should be employed to consolidate the findings presented in this paper

Frame-level features conveying phonetic information for language and speaker recognition

150 p.This Thesis, developed in the Software Technologies Working Group of the Departmentof Electricity and Electronics of the University of the Basque Country, focuseson the research eld of spoken language and speaker recognition technologies.More specically, the research carried out studies the design of a set of featuresconveying spectral acoustic and phonotactic information, searches for the optimalfeature extraction parameters, and analyses the integration and usage of the featuresin language recognition systems, and the complementarity of these approacheswith regard to state-of-the-art systems. The study reveals that systems trained onthe proposed set of features, denoted as Phone Log-Likelihood Ratios (PLLRs), arehighly competitive, outperforming in several benchmarks other state-of-the-art systems.Moreover, PLLR-based systems also provide complementary information withregard to other phonotactic and acoustic approaches, which makes them suitable infusions to improve the overall performance of spoken language recognition systems.The usage of this features is also studied in speaker recognition tasks. In this context,the results attained by the approaches based on PLLR features are not as remarkableas the ones of systems based on standard acoustic features, but they still providecomplementary information that can be used to enhance the overall performance ofthe speaker recognition systems

Children\u27s Sensitivity to Pitch Variation in Language

Children acquire consonant and vowel categories by 12 months, but take much longer to learn to interpret perceptible variation. This dissertation considers children’s interpretation of pitch variation. Pitch operates, often simultaneously, at different levels of linguistic structure. English-learning children must disregard pitch at the lexical level—since English is not a tone language—while still attending to pitch for its other functions. Chapters 1 and 5 outline the learning problem and suggest ways children might solve it. Chapter 2 demonstrates that 2.5-year-olds know pitch cannot differentiate words in English. Chapter 3 finds that not until age 4–5 do children correctly interpret pitch cues to emotions. Chapter 4 demonstrates some sensitivity between 2.5 and 5 years to the pitch cue to lexical stress, but continuing difficulties at the older ages. These findings suggest a late trajectory for interpretation of prosodic variation; throughout, I propose explanations for this protracted time-course