42 research outputs found
Using Phonological Phrase Segmentation to Improve Automatic Keyword Spotting for the Highly Agglutinating Hungarian Language
This paper investigates the usage of prosody for the improvement of keyword spotting, focusing on the highly agglutinating Hungarian language, where keyword spotting cannot be effectively performed using LVCSR, as such systems are either unavailable or hard to operate due to high OOV rates and poor N-gram language modelling capabilities. Therefore, the applied keyword spotting system is based on confidence scores computed as a ratio of acoustic scores obtained in two ways: firstly, by decoding with an universal background model; and secondly, by decoding with a keyword model embedded into filler models. Prosody is used to perform an automatic phonological phrase alignment for speech, proven to be useful for automatic partial word boundary detection in fixed stress languages. Several features deduced from the phonological phrase alignment are investigated to rescore baseline confidence scores both in a rule-based and in a data-driven manner. Results show that in relevant operating points of the system, a false alarm reduction of 10% - 40% can be reached by the same miss probability rates
PHONOTACTIC AND ACOUSTIC LANGUAGE RECOGNITION
Práce pojednává o fonotaktickém a akustickém přístupu pro automatické rozpoznávání jazyka. První část práce pojednává o fonotaktickém přístupu založeném na výskytu fonémových sekvenci v řeči. Nejdříve je prezentován popis vývoje fonémového rozpoznávače jako techniky pro přepis řeči do sekvence smysluplných symbolů. Hlavní důraz je kladen na dobré natrénování fonémového rozpoznávače a kombinaci výsledků z několika fonémových rozpoznávačů trénovaných na různých jazycích (Paralelní fonémové rozpoznávání následované jazykovými modely (PPRLM)). Práce také pojednává o nové technice anti-modely v PPRLM a studuje použití fonémových grafů místo nejlepšího přepisu. Na závěr práce jsou porovnány dva přístupy modelování výstupu fonémového rozpoznávače -- standardní n-gramové jazykové modely a binární rozhodovací stromy. Hlavní přínos v akustickém přístupu je diskriminativní modelování cílových modelů jazyků a první experimenty s kombinací diskriminativního trénování a na příznacích, kde byl odstraněn vliv kanálu. Práce dále zkoumá různé druhy technik fúzi akustického a fonotaktického přístupu. Všechny experimenty jsou provedeny na standardních datech z NIST evaluaci konané v letech 2003, 2005 a 2007, takže jsou přímo porovnatelné s výsledky ostatních skupin zabývajících se automatickým rozpoznáváním jazyka. S fúzí uvedených technik jsme posunuli state-of-the-art výsledky a dosáhli vynikajících výsledků ve dvou NIST evaluacích.This thesis deals with phonotactic and acoustic techniques for automatic language recognition (LRE). The first part of the thesis deals with the phonotactic language recognition based on co-occurrences of phone sequences in speech. A thorough study of phone recognition as tokenization technique for LRE is done, with focus on the amounts of training data for phone recognizer and on the combination of phone recognizers trained on several language (Parallel Phone Recognition followed by Language Model - PPRLM). The thesis also deals with novel technique of anti-models in PPRLM and investigates into using phone lattices instead of strings. The work on phonotactic approach is concluded by a comparison of classical n-gram modeling techniques and binary decision trees. The acoustic LRE was addressed too, with the main focus on discriminative techniques for training target language acoustic models and on initial (but successful) experiments with removing channel dependencies. We have also investigated into the fusion of phonotactic and acoustic approaches. All experiments were performed on standard data from NIST 2003, 2005 and 2007 evaluations so that the results are directly comparable to other laboratories in the LRE community. With the above mentioned techniques, the fused systems defined the state-of-the-art in the LRE field and reached excellent results in NIST evaluations.
Deep Neural Network Architectures for Large-scale, Robust and Small-Footprint Speaker and Language Recognition
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura : 27-04-2017Artificial neural networks are powerful learners of the information embedded in speech signals.
They can provide compact, multi-level, nonlinear representations of temporal sequences
and holistic optimization algorithms capable of surpassing former leading paradigms. Artificial
neural networks are, therefore, a promising technology that can be used to enhance our
ability to recognize speakers and languages–an ability increasingly in demand in the context
of new, voice-enabled interfaces used today by millions of users. The aim of this thesis is to
advance the state-of-the-art of language and speaker recognition through the formulation,
implementation and empirical analysis of novel approaches for large-scale and portable
speech interfaces. Its major contributions are: (1) novel, compact network architectures
for language and speaker recognition, including a variety of network topologies based on
fully-connected, recurrent, convolutional, and locally connected layers; (2) a bottleneck combination
strategy for classical and neural network approaches for long speech sequences; (3)
the architectural design of the first, public, multilingual, large vocabulary continuous speech
recognition system; and (4) a novel, end-to-end optimization algorithm for text-dependent
speaker recognition that is applicable to a range of verification tasks. Experimental results
have demonstrated that artificial neural networks can substantially reduce the number of
model parameters and surpass the performance of previous approaches to language and
speaker recognition, particularly in the cases of long short-term memory recurrent networks
(used to model the input speech signal), end-to-end optimization algorithms (used to predict
languages or speakers), short testing utterances, and large training data collections.Las redes neuronales artificiales son sistemas de aprendizaje capaces de extraer la información
embebida en las señales de voz. Son capaces de modelar de forma eficiente secuencias
temporales complejas, con información no lineal y distribuida en distintos niveles semanticos,
mediante el uso de algoritmos de optimización integral con la capacidad potencial de mejorar
los sistemas aprendizaje automático existentes. Las redes neuronales artificiales son, pues,
una tecnología prometedora para mejorar el reconocimiento automático de locutores e
idiomas; siendo el reconocimiento de de locutores e idiomas, tareas con cada vez más
demanda en los nuevos sistemas de control por voz, que ya utilizan millones de personas. Esta
tesis tiene como objetivo la mejora del estado del arte de las tecnologías de reconocimiento
de locutor y de idioma mediante la formulación, implementación y análisis empírico de
nuevos enfoques basados en redes neuronales, aplicables a dispositivos portátiles y a su uso
en gran escala. Las principales contribuciones de esta tesis incluyen la propuesta original de:
(1) arquitecturas eficientes que hacen uso de capas neuronales densas, localmente densas,
recurrentes y convolucionales; (2) una nueva estrategia de combinación de enfoques clásicos
y enfoques basados en el uso de las denominadas redes de cuello de botella; (3) el diseño del
primer sistema público de reconocimiento de voz, de vocabulario abierto y continuo, que es
además multilingüe; y (4) la propuesta de un nuevo algoritmo de optimización integral para
tareas de reconocimiento de locutor, aplicable también a otras tareas de verificación. Los
resultados experimentales extraídos de esta tesis han demostrado que las redes neuronales
artificiales son capaces de reducir el número de parámetros usados por los algoritmos de
reconocimiento tradicionales, así como de mejorar el rendimiento de dichos sistemas de
forma substancial. Dicha mejora relativa puede acentuarse a través del modelado de voz
mediante redes recurrentes de memoria a largo plazo, el uso de algoritmos de optimización
integral, el uso de locuciones de evaluation de corta duración y mediante la optimización del
sistema con grandes cantidades de datos de entrenamiento
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
Contributions to keyword spotting and spoken term: detection for information retrieval in audio minig
Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, marzo de 200
Application of automatic speech recognition technologies to singing
The research field of Music Information Retrieval is concerned with the automatic analysis of musical characteristics. One aspect that has not received much attention so far is the automatic analysis of sung lyrics. On the other hand, the field of Automatic Speech Recognition has produced many methods for the automatic analysis of speech, but those have rarely been employed for singing. This thesis analyzes the feasibility of applying various speech recognition methods to singing, and suggests adaptations. In addition, the routes to practical applications for these systems are described. Five tasks are considered: Phoneme recognition, language identification, keyword spotting, lyrics-to-audio alignment, and retrieval of lyrics from sung queries. The main bottleneck in almost all of these tasks lies in the recognition of phonemes from sung audio. Conventional models trained on speech do not perform well when applied to singing. Training models on singing is difficult due to a lack of annotated data. This thesis offers two approaches for generating such data sets. For the first one, speech recordings are made more “song-like”. In the second approach, textual lyrics are automatically aligned to an existing singing data set. In both cases, these new data sets are then used for training new acoustic models, offering considerable improvements over models trained on speech. Building on these improved acoustic models, speech recognition algorithms for the individual tasks were adapted to singing by either improving their robustness to the differing characteristics of singing, or by exploiting the specific features of singing performances. Examples of improving robustness include the use of keyword-filler HMMs for keyword spotting, an i-vector approach for language identification, and a method for alignment and lyrics retrieval that allows highly varying durations. Features of singing are utilized in various ways: In an approach for language identification that is well-suited for long recordings; in a method for keyword spotting based on phoneme durations in singing; and in an algorithm for alignment and retrieval that exploits known phoneme confusions in singing.Das Gebiet des Music Information Retrieval befasst sich mit der automatischen Analyse von musikalischen Charakteristika. Ein Aspekt, der bisher kaum erforscht wurde, ist dabei der gesungene Text. Auf der anderen Seite werden in der automatischen Spracherkennung viele Methoden für die automatische Analyse von Sprache entwickelt, jedoch selten für Gesang. Die vorliegende Arbeit untersucht die Anwendung von Methoden aus der Spracherkennung auf Gesang und beschreibt mögliche Anpassungen. Zudem werden Wege zur praktischen Anwendung dieser Ansätze aufgezeigt. Fünf Themen werden dabei betrachtet: Phonemerkennung, Sprachenidentifikation, Schlagwortsuche, Text-zu-Gesangs-Alignment und Suche von Texten anhand von gesungenen Anfragen. Das größte Hindernis bei fast allen dieser Themen ist die Erkennung von Phonemen aus Gesangsaufnahmen. Herkömmliche, auf Sprache trainierte Modelle, bieten keine guten Ergebnisse für Gesang. Das Trainieren von Modellen auf Gesang ist schwierig, da kaum annotierte Daten verfügbar sind. Diese Arbeit zeigt zwei Ansätze auf, um solche Daten zu generieren. Für den ersten wurden Sprachaufnahmen künstlich gesangsähnlicher gemacht. Für den zweiten wurden Texte automatisch zu einem vorhandenen Gesangsdatensatz zugeordnet. Die neuen Datensätze wurden zum Trainieren neuer Modelle genutzt, welche deutliche Verbesserungen gegenüber sprachbasierten Modellen bieten. Auf diesen verbesserten akustischen Modellen aufbauend wurden Algorithmen aus der Spracherkennung für die verschiedenen Aufgaben angepasst, entweder durch das Verbessern der Robustheit gegenüber Gesangscharakteristika oder durch das Ausnutzen von hilfreichen Besonderheiten von Gesang. Beispiele für die verbesserte Robustheit sind der Einsatz von Keyword-Filler-HMMs für die Schlagwortsuche, ein i-Vector-Ansatz für die Sprachenidentifikation sowie eine Methode für das Alignment und die Textsuche, die stark schwankende Phonemdauern nicht bestraft. Die Besonderheiten von Gesang werden auf verschiedene Weisen genutzt: So z.B. in einem Ansatz für die Sprachenidentifikation, der lange Aufnahmen benötigt; in einer Methode für die Schlagwortsuche, die bekannte Phonemdauern in Gesang mit einbezieht; und in einem Algorithmus für das Alignment und die Textsuche, der bekannte Phonemkonfusionen verwertet