ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages

We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting. This forms part of a United Nations effort using keyword spotting to support humanitarian relief programmes in parts of Africa where languages are severely under-resourced. We use 1920 isolated keywords (40 types, 34 minutes) as exemplars for dynamic time warping (DTW) template matching, which is performed on a much larger body of untranscribed speech. These DTW costs are used as targets for a convolutional neural network (CNN) keyword spotter, giving a much faster system than direct DTW. Here we consider how available data from well-resourced languages can improve this CNN-DTW approach. We show that multilingual BNFs trained on ten languages improve the area under the ROC curve of a CNN-DTW system by 10.9% absolute relative to the MFCC baseline. By combining low-resource DTW-based supervision with information from well-resourced languages, CNN-DTW is a competitive option for low-resource keyword spotting.Comment: 5 pages, 3 figures, 3 tables, 1 equation accepted at SLTU 201

A Systematic Study and Empirical Analysis of Lip Reading Models using Traditional and Deep Learning Algorithms

Despite the fact that there are many applications for analyzing and recreating the audio through existinglip movement recognition, the researchers have shown the interest in developing the automatic lip-readingsystems to achieve the increased performance. Modelling of the framework has been playing a major role inadvance yield of sequential framework. In recent years there have been lot of interest in Deep Neural Networks(DNN) and break through results in various domains including Image Classification, Speech Recognition andNatural Language Processing. To represents complex functions DNNs are used and also they play a vital rolein Automatic Lip Reading (ALR) systems. This paper mainly focuses on the traditional pixel, shape and mixedfeature extractions and their improved technologies for lip reading recognitions. It highlights the mostimportant techniques and progression from end-to-end deep learning architectures that were evolved duringthe past decade. The investigation points out the voice-visual databases that are used for analyzing and trainthe system with the most common words and the count of speakers and the size, length of the language andtime duration. On the flip side, ALR systems developed were compared with their old-style systems. Thestatistical analysis is performed to recognize the characters or numerals and words or sentences in English andcompared their performances

Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting

Used for simple voice commands and wake-word detection, keyword spotting (KWS) is the task of detecting pre-determined keywords in a stream of utterances. A common implementation of KWS involves transmitting audio samples over the network and detecting target keywords in the cloud with neural networks because on-device application development presents compatibility issues with various edge devices and provides limited supports for deep learning. Unfortunately, such an architecture can lead to unpleasant user experiences because network latency is not deterministic. Furthermore, the client-server architecture raises privacy concerns because users lose control over the audio data once it leaves the edge device. In this thesis, I present Honkling, a novel, JavaScript-based KWS system. Unlike previous KWS systems, Honkling operates purely on the client-side—Honkling is decentralized and serverless. Given that it is implemented in JavaScript, Honkling can be deployed directly in the browser, achieving higher compatibility and efficiency than the existing client-server architecture. From a comprehensive efficiency evaluation on desktops, laptops, and mobile devices, it is found that in-browser keyword detection only takes 0.5 seconds and achieves a high accuracy of 94% on the Google Speech Commands dataset. From an empirical study, the accuracy of Honkling is found to be inconsistent in practice due to different accents. To ensure high detection accuracy for every user, I explore fine-tuning the trained model with user personalized recordings. From my thorough experiments, it is found that such a process can increase the absolute accuracy up to 10% with only five recordings per keyword. Furthermore, the study shows that in-browser fine-tuning only takes eight seconds in the presence of hardware acceleration

Spotting Keywords in Offline Handwritten Documents Using Hausdorff Edit Distance

Keyword spotting has become a crucial topic in handwritten document recognition, by enabling content-based retrieval of scanned documents using search terms. With a query keyword, one can search and index the digitized handwriting which in turn facilitates understanding of manuscripts. Common automated techniques address the keyword spotting problem through statistical representations. Structural representations such as graphs apprehend the complex structure of handwriting. However, they are rarely used, particularly for keyword spotting techniques, due to high computational costs. The graph edit distance, a powerful and versatile method for matching any type of labeled graph, has exponential time complexity to calculate the similarities of graphs. Hence, the use of graph edit distance is constrained to small size graphs. The recently developed Hausdorff edit distance algorithm approximates the graph edit distance with quadratic time complexity by efficiently matching local substructures. This dissertation speculates using Hausdorff edit distance could be a promising alternative to other template-based keyword spotting approaches in term of computational time and accuracy. Accordingly, the core contribution of this thesis is investigation and development of a graph-based keyword spotting technique based on the Hausdorff edit distance algorithm. The high representational power of graphs combined with the efficiency of the Hausdorff edit distance for graph matching achieves remarkable speedup as well as accuracy. In a comprehensive experimental evaluation, we demonstrate the solid performance of the proposed graph-based method when compared with state of the art, both, concerning precision and speed. The second contribution of this thesis is a keyword spotting technique which incorporates dynamic time warping and Hausdorff edit distance approaches. The structural representation of graph-based approach combined with statistical geometric features representation compliments each other in order to provide a more accurate system. The proposed system has been extensively evaluated with four types of handwriting graphs and geometric features vectors on benchmark datasets. The experiments demonstrate a performance boost in which outperforms individual systems

Apprentissage profond de formes manuscrites pour la reconnaissance et le repérage efficace de l'écriture dans les documents numérisés

Malgré les efforts importants de la communauté d’analyse de documents, définir une representation robuste pour les formes manuscrites demeure un défi de taille. Une telle representation ne peut pas être définie explicitement par un ensemble de règles, et doit plutôt être obtenue avec une extraction intelligente de caractéristiques de haut niveau à partir d’images de documents. Dans cette thèse, les modèles d’apprentissage profond sont investigués pour la representation automatique de formes manuscrites. Les représentations proposées par ces modèles sont utilisées pour définir un système de reconnaissance et de repérage de mots individuels dans les documents. Le choix de traiter les mots individuellement est motivé par le fait que n’importe quel texte peut être segmenté en un ensemble de mots séparés. Dans une première contribution, une représentation non supervisée profonde est proposée pour la tâche de repérage de mots manuscrits. Cette représentation se base sur l’algorithme de regroupement spherical k-means, qui est employé pour construire une hiérarchie de fonctions paramétriques encodant les images de documents. Les avantages de cette représentation sont multiples. Tout d’abord, elle est définie de manière non supervisée, ce qui évite la nécessité d’avoir des données annotées pour l’entraînement. Ensuite, elle se calcule rapidement et est de taille compacte, permettant ainsi de repérer des mots efficacement. Dans une deuxième contribution, un modèle de bout en bout est développé pour la reconnaissance de mots manuscrits. Ce modèle est composé d’un réseau de neurones convolutifs qui prend en entrée l’image d’un mot et produit en sortie une représentation du texte reconnu. Ce texte est représenté sous la forme d’un ensemble de sous-sequences bidirectionnelles de caractères formant une hiérarchie. Cette représentation se distingue des approches existantes dans la littérature et offre plusieurs avantages par rapport à celles-ci. Notamment, elle est binaire et a une taille fixe, ce qui la rend robuste à la taille du texte. Par ailleurs, elle capture la distribution des sous-séquences de caractères dans le corpus d’entraînement, et permet donc au modèle entraîné de transférer cette connaissance à de nouveaux mots contenant les memes sous-séquences. Dans une troisième et dernière contribution, un modèle de bout en bout est proposé pour résoudre simultanément les tâches de repérage et de reconnaissance. Ce modèle intègre conjointement les textes et les images de mots dans un seul espace vectoriel. Une image est projetée dans cet espace via un réseau de neurones convolutifs entraîné à détecter les différentes forms de caractères. De même, un mot est projeté dans cet espace via un réseau de neurones récurrents. Le modèle proposé est entraîné de manière à ce que l’image d’un mot et son texte soient projetés au même point. Dans l’espace vectoriel appris, les tâches de repérage et de reconnaissance peuvent être traitées efficacement comme un problème de recherche des plus proches voisins

Machine Learning Methods for Speech Recognition

Tato diplomová práce se zabývá využitím metod strojového učení pro rozpoznávání řeči. První část se věnuje teoretickému popisu metod zpracování řečového signálu a algoritmům, které je možné použít pro automatické rozpoznávání řeči. Jsou zde popsány algoritmy dynamického borcení času, skryté Markovovy modely a hluboké neuronové sítě. Praktická část byla věnována realizaci ASR systému pro izolovaná slova, který je založen na konvolučních neuronových sítích. Byly použity nástroje Keras a TensorFlow pro programovací jazyk Python. Pro trénování a testování byla použita databáze 15 slov, obsahující promluvy od žen a můžu v~různém prostředí. Využití systému je možné různých oblastech Industry 4.0.This thesis is devoted to machine learning methods for speech recognition. The first part deals with teoretical description of methods for speech signal processing and algorithms which can be used for automatic speech recognition. Dynamic time warping, hidden Markov models and deep neural networks are described here. The practical part is focused on the description of the created system, which is based on convolutional neural networks. This system was designed and implemented in Python using Keras and TensorFlow. A~dataset of 15 words was used for training and testing. The use of the system is possible in various areas of Industry 4.0.440 - Katedra telekomunikační technikyvýborn

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