Relevant bibliographies by topics / Keyword spotting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Keyword spotting'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Keyword spotting.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Keyword spotting"

1

Keshet, Joseph, David Grangier, and Samy Bengio. "Discriminative keyword spotting." Speech Communication 51, no. 4 (April 2009): 317–29. http://dx.doi.org/10.1016/j.specom.2008.10.002.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Retsinas, George, Georgios Louloudis, Nikolaos Stamatopoulos, and Basilis Gatos. "Efficient Learning-Free Keyword Spotting." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 7 (July 1, 2019): 1587–600. http://dx.doi.org/10.1109/tpami.2018.2845880.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Liu, Zuozhen, Ta Li, and Pengyuan Zhang. "Neural keyword confidence estimation for open‐vocabulary keyword spotting." Electronics Letters 58, no. 3 (November 27, 2021): 133–35. http://dx.doi.org/10.1049/ell2.12368.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Lopez-Espejo, Ivan, Zheng-Hua Tan, John H. L. Hansen, and Jesper Jensen. "Deep Spoken Keyword Spotting: An Overview." IEEE Access 10 (2022): 4169–99. http://dx.doi.org/10.1109/access.2021.3139508.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Retsinas, George, Giorgos Sfikas, and Basilis Gatos. "Transferable Deep Features for Keyword Spotting." Proceedings 2, no. 2 (January 9, 2018): 89. http://dx.doi.org/10.3390/proceedings2020089.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Brik, Youcef. "Mental model for handwritten keyword spotting." Journal of Electronic Imaging 27, no. 05 (October 4, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.5.053027.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yamashita, Yoichi, Daisuke Iwahashi, and Riichiro Mizoguchi. "Keyword spotting using F0 contour information." Systems and Computers in Japan 32, no. 7 (2001): 52–61. http://dx.doi.org/10.1002/scj.1041.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Khan, Wasiq, and Rob Holton. "Decision Support System for Keyword Spotting Using Theory of Evidence." International Journal of Computer and Electrical Engineering 8, no. 1 (2016): 22–30. http://dx.doi.org/10.17706/ijcee.2016.8.1.22-30.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Rebai, Ilyes, Yassine BenAyed, and Walid Mahdi. "A novel keyword rescoring method for improved spoken keyword spotting." Procedia Computer Science 126 (2018): 312–20. http://dx.doi.org/10.1016/j.procs.2018.07.265.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kanadje, Manish, Zachary Miller, Anurag Agarwal, Roger Gaborski, Richard Zanibbi, and Stephanie Ludi. "Assisted keyword indexing for lecture videos using unsupervised keyword spotting." Pattern Recognition Letters 71 (February 2016): 8–15. http://dx.doi.org/10.1016/j.patrec.2015.11.012.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Keyword spotting"

1

Skácel, Miroslav. "Query-by-Example Keyword Spotting." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234939.

Full text
Abstract:
Tato diplomová práce se zabývá moderními přístupy detekce klíčových slov a detekce frází v řečových datech. V úvodní části je seznámení s problematikou a teoretický popis metod pro detekci. Následuje popis reprezentace vstupních datových sad použitých při experimentech a evaluaci. Dále jsou uvedeny metody pro detekci klíčových slov definovaných vzorem. Následně jsou popsány evaluační metody a techniky použité pro skórování. Po provedení experimentů na datových sadách a po evaluaci jsou diskutovány výsledky. V dalším kroku jsou navrženy a poté implementovány moderní postupy vedoucí k vylepšení systému pro detekci a opět je provedena evaluace a diskuze dosažených výsledků. V závěrečné části je práce zhodnocena a jsou zde navrženy další směy vývoje našeho systému. Příloha obsahuje manuál pro používání implementovaných skriptů.
APA, Harvard, Vancouver, ISO, and other styles
2

Sunde, Valfridsson Jonas. "Query By Example Keyword Spotting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299743.

Full text
Abstract:
Voice user interfaces have been growing in popularity and with them an interest for open vocabulary keyword spotting. In this thesis we focus on one particular approach to open vocabulary keyword spotting, query by example keyword spotting. Three types of query by example keyword spotting approaches are described and evaluated: sequence distances, speech to phonemes and deep distance learning. Evaluation is done on a series of custom tasks designed to measure a variety of aspects. The Google Speech Commands benchmark is used for evaluation as well, this to make it more comparable to existing works. From the results, the deep distance learning approach seem most promising in most environments except when memory is very constrained; in which sequence distances might be considered. The speech to phonemes methods is lacking in the usability evaluation.
Röstgränssnitt har växt i populäritet och med dem ett intresse för öppenvokabulärnyckelordsigenkänning. I den här uppsatsen fokuserar vi på en specifik form av öppenvokabulärnyckelordsigenkänning, den s.k nyckelordsigenkänning- genom- exempel. Tre typer av nyckelordsigenkänning- genom- exempel metoder beskrivs och utvärderas: sekvensavstånd, tal till fonem samt djupavståndsinlärning. Utvärdering görs på konstruerade uppgifter designade att mäta en mängd olika aspekter hos metoderna. Google Speech Commands data används för utvärderingen också, detta för att göra det mer jämförbart mot existerade arbeten. Från resultaten framgår det att djupavståndsinlärning verkar mest lovande förutom i miljöer där resurser är väldigt begränsade; i dessa kan sekvensavstånd vara av intresse. Tal till fonem metoderna visar brister i användningsuvärderingen.
APA, Harvard, Vancouver, ISO, and other styles
3

Ling, Yong. "Keyword spotting in continuous speech utterances." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0024/MQ50822.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ling, Yong 1973. "Keyword spotting in continuous speech utterances." Thesis, McGill University, 1999. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21595.

Full text
Abstract:
The work in this thesis constructed a word spotting system, which managed to spot an amount of pre-defined keywords out of unconstrained running conversational speech utterances. The development and experiments are based on the Credit Card subset of SWITCHBOARD speech corpus. The techniques are applied in the context of a Hidden Markov Model (HMM) based Continuous Speech Recognition (CSR) approach to keyword spotting. The word spotting system uses context-dependent acoustic triphone to model both keyword and non-keyword speech utterances. To enhance the true keyword spotting rate, sophisticated keyword-filler network topology models are defined in two different orthographic ways, individual phonemic filler models and individual syllabic filler models. To introduce more lexical constraints, a bigram language model is used. Better performance is obtained in the system with more lexical constraints. A background acoustic model is paralleled to the system network to account for the acoustic variety. The results of the experiments show that the word spotting rate of the overall performance increased by 84% when more lexical constraints applied, and the merge of the background model helps to increase the spotting rate by 5.73%.
APA, Harvard, Vancouver, ISO, and other styles
5

Puigcerver, I. Pérez Joan. "A Probabilistic Formulation of Keyword Spotting." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/116834.

Full text
Abstract:
[ES] La detección de palabras clave (Keyword Spotting, en inglés), aplicada a documentos de texto manuscrito, tiene como objetivo recuperar los documentos, o partes de ellos, que sean relevantes para una cierta consulta (query, en inglés), indicada por el usuario, entre una gran colección de documentos. La temática ha recogido un gran interés en los últimos 20 años entre investigadores en Reconocimiento de Formas (Pattern Recognition), así como bibliotecas y archivos digitales. Esta tesis, en primer lugar, define el objetivo de la detección de palabras clave a partir de una perspectiva basada en la Teoría de la Decisión y una formulación probabilística adecuada. Más concretamente, la detección de palabras clave se presenta como un caso particular de Recuperación de la Información (Information Retrieval), donde el contenido de los documentos es desconocido, pero puede ser modelado mediante una distribución de probabilidad. Además, la tesis también demuestra que, bajo las distribuciones de probabilidad correctas, el marco de trabajo desarrollada conduce a la solución óptima del problema, según múltiples medidas de evaluación utilizadas tradicionalmente en el campo. Más tarde, se utilizan distintos modelos estadísticos para representar las distribuciones necesarias: Redes Neuronales Recurrentes o Modelos Ocultos de Markov. Los parámetros de estos son estimados a partir de datos de entrenamiento, y las respectivas distribuciones son representadas mediante Transductores de Estados Finitos con Pesos (Weighted Finite State Transducers). Con el objetivo de hacer que el marco de trabajo sea práctico en grandes colecciones de documentos, se presentan distintos algoritmos para construir índices de palabras a partir de modelos probabilísticos, basados tanto en un léxico cerrado como abierto. Estos índices son muy similares a los utilizados por los motores de búsqueda tradicionales. Además, se estudia la relación que hay entre la formulación probabilística presentada y otros métodos de gran influencia en el campo de la detección de palabras clave, destacando cuáles son las limitaciones de los segundos. Finalmente, todas la aportaciones se evalúan de forma experimental, no sólo utilizando pruebas académicas estándar, sino también en colecciones con decenas de miles de páginas provenientes de manuscritos históricos. Los resultados muestran que el marco de trabajo presentado permite construir sistemas de detección de palabras clave muy rápidos y precisos, con una sólida base teórica.
[CAT] La detecció de paraules clau (Keyword Spotting, en anglès), aplicada a documents de text manuscrit, té com a objectiu recuperar els documents, o parts d'ells, que siguen rellevants per a una certa consulta (query, en anglès), indicada per l'usuari, dintre d'una gran col·lecció de documents. La temàtica ha recollit un gran interés en els últims 20 anys entre investigadors en Reconeixement de Formes (Pattern Recognition), així com biblioteques i arxius digitals. Aquesta tesi defineix l'objectiu de la detecció de paraules claus a partir d'una perspectiva basada en la Teoria de la Decisió i una formulació probabilística adequada. Més concretament, la detecció de paraules clau es presenta com un cas concret de Recuperació de la Informació (Information Retrieval), on el contingut dels documents és desconegut, però pot ser modelat mitjançant una distribució de probabilitat. A més, la tesi també demostra que, sota les distribucions de probabilitat correctes, el marc de treball desenvolupat condueix a la solució òptima del problema, segons diverses mesures d'avaluació utilitzades tradicionalment en el camp. Després, diferents models estadístics s'utilitzen per representar les distribucions necessàries: Xarxes Neuronal Recurrents i Models Ocults de Markov. Els paràmetres d'aquests són estimats a partir de dades d'entrenament, i les corresponents distribucions són representades mitjançant Transductors d'Estats Finits amb Pesos (Weighted Finite State Transducers). Amb l'objectiu de fer el marc de treball útil per a grans col·leccions de documents, es presenten distints algorismes per construir índexs de paraules a partir dels models probabilístics, tan basats en un lèxic tancat com en un obert. Aquests índexs són molt semblants als utilitzats per motors de cerca tradicionals. A més a més, s'estudia la relació que hi ha entre la formulació probabilística presentada i altres mètodes de gran influència en el camp de la detecció de paraules clau, destacant algunes limitacions dels segons. Finalment, totes les aportacions s'avaluen de forma experimental, no sols utilitzant proves acadèmics estàndard, sinó també en col·leccions amb desenes de milers de pàgines provinents de manuscrits històrics. Els resultats mostren que el marc de treball presentat permet construir sistemes de detecció de paraules clau molt acurats i ràpids, amb una sòlida base teòrica.
[EN] Keyword Spotting, applied to handwritten text documents, aims to retrieve the documents, or parts of them, that are relevant for a query, given by the user, within a large collection of documents. The topic has gained a large interest in the last 20 years among Pattern Recognition researchers, as well as digital libraries and archives. This thesis, first defines the goal of Keyword Spotting from a Decision Theory perspective. Then, the problem is tackled following a probabilistic formulation. More precisely, Keyword Spotting is presented as a particular instance of Information Retrieval, where the content of the documents is unknown, but can be modeled by a probability distribution. In addition, the thesis also proves that, under the correct probability distributions, the framework provides the optimal solution, under many of the evaluation measures traditionally used in the field. Later, different statistical models are used to represent the probability distribution over the content of the documents. These models, Hidden Markov Models or Recurrent Neural Networks, are estimated from training data, and the corresponding distributions over the transcripts of the images can be efficiently represented using Weighted Finite State Transducers. In order to make the framework practical for large collections of documents, this thesis presents several algorithms to build probabilistic word indexes, using both lexicon-based and lexicon-free models. These indexes are very similar to the ones used by traditional search engines. Furthermore, we study the relationship between the presented formulation and other seminal approaches in the field of Keyword Spotting, highlighting some limitations of the latter. Finally, all the contributions are evaluated experimentally, not only on standard academic benchmarks, but also on collections including tens of thousands of pages of historical manuscripts. The results show that the proposed framework and algorithms allow to build very accurate and very fast Keyword Spotting systems, with a solid underlying theory.
Puigcerver I Pérez, J. (2018). A Probabilistic Formulation of Keyword Spotting [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/116834
TESIS
APA, Harvard, Vancouver, ISO, and other styles
6

Wang, Miaorong. "Algorithms and low power hardware for keyword spotting." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118035.

Full text
Abstract:
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-76).
Keyword spotting (KWS) is widely used in mobile devices to provide hands-free interface. It continuously listens to all sound signals, detects specific keywords and triggers the downstream system. The key design target of a KWS system is to achieve high classification accuracy of specified keywords and have low power consumption while doing real-time processing of speech data. The algorithm based on convolutional neural network (CNN) delivers high accuracy with small model size that can be stored in on-chip memory. However, the state-of-the-art NN accelerators either target at complex tasks using large CNN models, e.g. AlexNet, or support limited neural network (NN) architectures which delivers lower classification accuracy for KWS. This thesis takes an algorithm-and-hardware co-design approach to implement a low power NN accelerator for the KWS system that is able to process CNN with flexible structures. On the algorithm side, we propose a weight tuning method that tweaks the bits of weights to lower the switching activity in the weight network-on-chip (NoC) and multipliers. The algorithm takes in 2's complement 8-bit original weights and outputs sign-magnitude 8-bit tuned weights. In our experiment, 60.96% reduction in the toggle count of weights is achieved with 0.75% loss in accuracy. On the hardware side, we implement a processing element (PE) to efficiently process the tuned weights. It takes in sign-magnitude weights and input activations, and multiplies them by an unsigned multiplier. An XOR gate is used to generate the sign bit of the product. The sign-magnitude product is converted back to 2's complement representation and accumulated using an adder-and-subtractor. The sign bit of the product is used as a carry bit to do the conversion. Comparing to the PE that processes original 2's complement weights, around 35% power reduction is observed. In the end, this thesis presents a CNN accelerator that consumes 1.2 mW when doing real-time processing of speech data with an accuracy of around 87.3% on Google speech command dataset [34].
by Miaorong Wang.
S.M.
APA, Harvard, Vancouver, ISO, and other styles
7

Friesch, Pius. "Generating Training Data for Keyword Spotting given Few Samples." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254960.

Full text
Abstract:
Speech recognition systems generally need a large quantity of highly variable voice and recording conditions in order to produce robust results. In the specific case of keyword spotting, where only short commands are recognized instead of large vocabularies, the resource-intensive task of data acquisition has to be repeated for each keyword individually. Over the past few years, neural methods in speech synthesis and voice conversion made tremendous progress and generate samples that are realistic to the human ear. In this work, we explore the feasibility of using such methods to generate training data for keyword spotting methods. In detail, we want to evaluate if the generated samples are indeed realistic or only sound so and if a model trained on these generated samples can generalize to real samples. We evaluated three neural network speech synthesis and voice conversion techniques : (1) Speaker Adaptive VoiceLoop, (2) Factorized Hierarchical Variational Autoencoder (FHVAE), (3) Vector Quantised-Variational AutoEncoder (VQVAE). These three methods are evaluated as data augmentation or data generation techniques on a keyword spotting task. The performance of the models is compared to a baseline of changing the pitch, tempo, and speed of the original sample. The experiments show that using the neural network techniques can provide an up to 20% relative accuracy improvement on the validation set. The baseline augmentation technique performs at least twice as good. This seems to indicate that using multi-speaker speech synthesis or voice conversation naively does not yield varied or realistic enough samples.
Taligenkänningssystem behöver generellt en stor mängd träningsdata med varierande röstoch inspelningsförhållanden för att ge robusta resultat. I det specifika fallet med nyckelordsidentifiering, där endast korta kommandon känns igen i stället för stora vokabulärer, måste resurskrävande datainsamling göras för varje sökord individuellt. Under de senaste åren har neurala metoder i talsyntes och röstkonvertering gjort stora framsteg och genererar tal som är realistiskt för det mänskliga örat. I det här arbetet undersöker vi möjligheten att använda sådana metoder för att generera träningsdata för nyckelordsidentifiering. I detalj vill vi utvärdera om det genererade träningsdatat verkligen är realistiskt eller bara låter så, och om en modell tränad på dessa genererade exempel generaliserar väl till verkligt tal. Vi utvärderade tre metoder för neural talsyntes och röstomvandlingsteknik: (1) Speaker Adaptive VoiceLoop, (2) Factorized Hierarchical Variational Autoencoder (FHVAE), (3) Vector Quantised-Variational AutoEncoder (VQVAE).Dessa tre metoder används för att antingen generera träningsdata från text (talsyntes) eller att berika ett befintligt dataset för att simulera flera olika talare med hjälp av röstkonvertering, och utvärderas i ett system för nyckelordsidentifiering. Modellernas prestanda jämförs med en baslinje baserad på traditionell signalbehandling där tonhöjd och tempo varieras i det ursprungliga träningsdatat. Experimenten visar att man med hjälp av neurala nätverksmetoder kan ge en upp till 20% relativ noggrannhetsförbättring på valideringsuppsättningen jämfört med ursprungligt träningsdata. Baslinjemetoden baserad på signalbehandling ger minst dubbelt så bra resultat. Detta tycks indikera att användningen av talsyntes eller röstkonvertering med flera talare inte ger tillräckligt varierade eller representativa träningsdata.
APA, Harvard, Vancouver, ISO, and other styles
8

Zhang, Yaodong Ph D. Massachusetts Institute of Technology. "Unsupervised spoken keyword spotting and learning of acoustically meaningful units." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54655.

Full text
Abstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 103-106).
The problem of keyword spotting in audio data has been explored for many years. Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the majority of languages in the world. This thesis addresses this lack-of-annotation problem and presents two completely unsupervised spoken keyword spotting systems that do not require any transcribed data. In the first system, a Gaussian Mixture Model is trained to label speech frames with a Gaussian posteriorgram, without any transcription information. Given several spoken samples of a keyword, a segmental dynamic time warping is used to compare the Gaussian posteriorgrams between keyword samples and test utterances. The keyword detection result is then obtained by ranking the distortion scores of all the test utterances. In the second system, to avoid the need for spoken samples, a Joint-Multigram model is used to build a mapping from the keyword text samples to the Gaussian component indices. A keyword instance in the test data can be detected by calculating the similarity score of the Gaussian component index sequences between keyword samples and test utterances. The proposed two systems are evaluated on the TIMIT and MIT Lecture corpus. The result demonstrates the viability and effectiveness of the two systems. Furthermore, encouraged by the success of using unsupervised methods to perform keyword spotting, we present some preliminary investigation on the unsupervised detection of acoustically meaningful units in speech.
by Yaodong Zhang.
S.M.
APA, Harvard, Vancouver, ISO, and other styles
9

Narasimhan, Karthik Rajagopal. "Morphological segmentation : an unsupervised method and application to Keyword Spotting." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90139.

Full text
Abstract:
Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
26
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-44).
The contributions of this thesis are twofold. First, we present a new unsupervised algorithm for morphological segmentation that utilizes pseudo-semantic information, in addition to orthographic cues. We make use of the semantic signals from continuous word vectors, trained on huge corpora of raw text data. We formulate a log-linear model that is simple and can be used to perform fast, efficient inference on new words. We evaluate our model on a standard morphological segmentation dataset, and obtain large performance gains of up to 18.4% over an existing state-of-the-art system, Morfessor. Second, we explore the impact of morphological segmentation on the speech recognition task of Keyword Spotting (KWS). Despite potential benefits, state-of-the-art KWS systems do not use morphological information. In this thesis, we augment a KWS system with sub-word units derived by multiple segmentation algorithms including supervised and unsupervised morphological segmentations, along with phonetic and syllabic segmentations. Our experiments demonstrate that morphemes improve overall performance of KWS systems. Syllabic units, however, rival the performance of morphological units when used in KWS. By combining morphological and syllabic segmentations, we demonstrate substantial performance gains..
by Karthik Rajagopal Narasimhan.
S.M. in Computer Science and Engineering
APA, Harvard, Vancouver, ISO, and other styles
10

Thambiratnam, Albert J. K. "Acoustic keyword spotting in speech with applications to data mining." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/37254/1/Albert_Thambiratnam_Thesis.pdf.

Full text
Abstract:
Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Keyword spotting"

1

Stauffer, Michael, Andreas Fischer, and Kaspar Riesen. Graph-Based Keyword Spotting. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/11452.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Keyword spotting"

1

Moyal, Ami, Vered Aharonson, Ella Tetariy, and Michal Gishri. "Keyword Spotting Methods." In SpringerBriefs in Electrical and Computer Engineering, 7–11. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6489-1_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Mary, Leena, and Deekshitha G. "Keyword Spotting Techniques." In SpringerBriefs in Speech Technology, 45–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97761-4_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Veiga, Arlindo, Carla Lopes, Luís Sá, and Fernando Perdigão. "Acoustic Similarity Scores for Keyword Spotting." In Lecture Notes in Computer Science, 48–58. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09761-9_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bhardwaj, Anurag, Srirangaraj Setlur, and Venu Govindaraju. "Keyword Spotting Techniques for Sanskrit Documents." In Lecture Notes in Computer Science, 403–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00155-0_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Moyal, Ami, Vered Aharonson, Ella Tetariy, and Michal Gishri. "Keyword Spotting Out of Continuous Speech." In SpringerBriefs in Electrical and Computer Engineering, 1–6. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6489-1_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ayed, Yassine Ben, Dominique Fohr, Jean Paul Haton, and Gérard Chollet. "Keyword Spotting Using Support Vector Machines." In Text, Speech and Dialogue, 285–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46154-x_39.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Sfikas, Giorgos, George Retsinas, Angelos P. Giotis, Basilis Gatos, and Christophoros Nikou. "Keyword Spotting with Quaternionic ResNet: Application to Spotting in Greek Manuscripts." In Document Analysis Systems, 382–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lleida, E., J. B. Mariño, J. Salavedra, and A. Moreno. "Keyword Spotting, an Application for Voice Dialing." In Speech Recognition and Coding, 276–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57745-1_40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Greibus, Mindaugas, and Laimutis Telksnys. "Speech Keyword Spotting with Rule Based Segmentation." In Communications in Computer and Information Science, 186–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41947-8_17.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Gales, Mark J. F., Kate M. Knill, and Anton Ragni. "Low-Resource Speech Recognition and Keyword-Spotting." In Speech and Computer, 3–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66429-3_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Keyword spotting"

1

Haznedaroglu, Ali, Osman Buyuk, and Levent M. Arslan. "Keyword Spotting using Keyword Adapted Language Model." In 2007 15th IEEE Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298570.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, Qiyu, Weibin Zhang, Xiangmin Xu, and Xiaofen Xing. "Improved keyword spotting based on keyword/garbage models." In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2016. http://dx.doi.org/10.1109/apsipa.2016.7820743.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Li, L., S. J. Lu, and C. L. Tan. "A Fast Keyword-Spotting Technique." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4378677.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bahi, Halima, and Nadia Benati. "A new keyword spotting approach." In 2009 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2009. http://dx.doi.org/10.1109/mmcs.2009.5256728.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Huh, Jaesung, Minjae Lee, Heesoo Heo, Seongkyu Mun, and Joon Son Chung. "Metric Learning for Keyword Spotting." In 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021. http://dx.doi.org/10.1109/slt48900.2021.9383571.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Leroy, David, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau. "Federated Learning for Keyword Spotting." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683546.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Odame, Kofi, and Maria Nyamukuru. "Analog LSTM for Keyword Spotting." In 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2022. http://dx.doi.org/10.1109/aicas54282.2022.9869922.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Jose, Christin, Joe Wang, Grant Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, and Brian Kulis. "Latency Control for Keyword Spotting." In Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-10608.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Narasimhan, Karthik, Damianos Karakos, Richard Schwartz, Stavros Tsakalidis, and Regina Barzilay. "Morphological Segmentation for Keyword Spotting." In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/d14-1095.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Shen, Wenzhu, Ji Wu, and Wei Li. "Web-based keyword adapted Language Modeling for Keyword Spotting." In 2010 7th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2010. http://dx.doi.org/10.1109/iscslp.2010.5684898.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Keyword spotting"

1

Mitra, Vikramjit, Julien van Hout, Horacio Franco, Dimitra Vergyri, Yun Lei, Martin Graciarena, Yik-Cheung Tam, and Jing Zheng. Feature Fusion for High-Accuracy Keyword Spotting. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada613972.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hansen, John H. Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment. Fort Belvoir, VA: Defense Technical Information Center, October 2015. http://dx.doi.org/10.21236/ada623029.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Keyword spotting' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography