Les applications de la famille PETRALEX Speech Communication – gestionnaires multi-utilisateurs multifonctionnels du signal audio entrant et sortant dans les systèmes de communications vocales – sont conçues pour équiper les centres d’appel et de contact ; elles sont utilisées pour améliorer l’efficacité des communications d’affaires.
http://petralexsolutions.com
info@petralexsolutions.com
Performance Improvisation of Automatic Speaker Recognition by Spectral Reverb...Dvizma Sinha
Contemporary speaker recognition systems are prone to errors due reverberation when operated in closed environments. The project focuses on mitigating the effects of reverberation on Automatic Speaker recognition system. Implemented in MATLAB, the system uses Gaussian Mixture Model and Maximum Likelihood Criteria for feature (MFCC) matching and exponential envelope removal for reverberation mitigation. An average improvement of 16% in recognition rate was observed. The project is to be further improved for blind mitigation.
Timbral modeling for music artist recognition using i-vectorsHamid Eghbal-zadeh
This document summarizes a research paper on using i-vectors for artist recognition in music. The proposed method extracts i-vectors from songs to obtain a compact representation that captures artist variability. It uses a Gaussian mixture model (GMM) to calculate statistics from frame-level features, then performs factor analysis to extract i-vectors from the GMM supervectors. Experiments on a dataset of 20 artists show the method achieves better artist recognition performance than baselines, and works well across different backends like discriminant analysis and probabilistic linear discriminant analysis.
This document summarizes research on speaker recognition technologies. It discusses how speaker recognition can be used for biometric authentication by analyzing a person's voiceprint. It reviews literature on MFCC-GMM models for text-independent speaker verification and the use of speaker recognition in biometric security systems. The document also outlines the basic components of a speaker recognition system, including enrollment, feature extraction using MFCCs, and verification through comparison to stored voice templates using algorithms like GMM.
This document summarizes a survey on speaker recognition systems. It outlines 30 literature sources on topics like using computational auditory scene analysis, independent component analysis, and probabilistic linear discriminant analysis for speaker identification and verification. It also discusses challenges like robustness to noise and variability in recording conditions. The conclusion notes that variability from speakers and channels remains problematic and more work is needed to develop features stable over time and insensitive to variations in speaking style.
Identification d'une empreinte vocale pour les NulsAmaury Crickx
Présentation Devoxx France 2014
Les nouvelles perspectives offertes par la reconnaissance vocale vont tôt ou tard nous confronter à l'usage de librairies spécialisées dont le fonctionnement interne nous échappe totalement. Comment dès lors les évaluer, les utiliser correctement et en tirer le meilleur parti ?
Cette présentation ludique et pratique a pour objectif de démystifier les arcanes de l'analyse de la voix humaine et ses contraintes en vous présentant les rouages internes du logiciel libre "Recognito", créé par le conférencier et permettant l'identification d'un locuteur à partir de son empreinte vocale.
Les applications de la famille PETRALEX Speech Communication – gestionnaires multi-utilisateurs multifonctionnels du signal audio entrant et sortant dans les systèmes de communications vocales – sont conçues pour équiper les centres d’appel et de contact ; elles sont utilisées pour améliorer l’efficacité des communications d’affaires.
http://petralexsolutions.com
info@petralexsolutions.com
Performance Improvisation of Automatic Speaker Recognition by Spectral Reverb...Dvizma Sinha
Contemporary speaker recognition systems are prone to errors due reverberation when operated in closed environments. The project focuses on mitigating the effects of reverberation on Automatic Speaker recognition system. Implemented in MATLAB, the system uses Gaussian Mixture Model and Maximum Likelihood Criteria for feature (MFCC) matching and exponential envelope removal for reverberation mitigation. An average improvement of 16% in recognition rate was observed. The project is to be further improved for blind mitigation.
Timbral modeling for music artist recognition using i-vectorsHamid Eghbal-zadeh
This document summarizes a research paper on using i-vectors for artist recognition in music. The proposed method extracts i-vectors from songs to obtain a compact representation that captures artist variability. It uses a Gaussian mixture model (GMM) to calculate statistics from frame-level features, then performs factor analysis to extract i-vectors from the GMM supervectors. Experiments on a dataset of 20 artists show the method achieves better artist recognition performance than baselines, and works well across different backends like discriminant analysis and probabilistic linear discriminant analysis.
This document summarizes research on speaker recognition technologies. It discusses how speaker recognition can be used for biometric authentication by analyzing a person's voiceprint. It reviews literature on MFCC-GMM models for text-independent speaker verification and the use of speaker recognition in biometric security systems. The document also outlines the basic components of a speaker recognition system, including enrollment, feature extraction using MFCCs, and verification through comparison to stored voice templates using algorithms like GMM.
This document summarizes a survey on speaker recognition systems. It outlines 30 literature sources on topics like using computational auditory scene analysis, independent component analysis, and probabilistic linear discriminant analysis for speaker identification and verification. It also discusses challenges like robustness to noise and variability in recording conditions. The conclusion notes that variability from speakers and channels remains problematic and more work is needed to develop features stable over time and insensitive to variations in speaking style.
Identification d'une empreinte vocale pour les NulsAmaury Crickx
Présentation Devoxx France 2014
Les nouvelles perspectives offertes par la reconnaissance vocale vont tôt ou tard nous confronter à l'usage de librairies spécialisées dont le fonctionnement interne nous échappe totalement. Comment dès lors les évaluer, les utiliser correctement et en tirer le meilleur parti ?
Cette présentation ludique et pratique a pour objectif de démystifier les arcanes de l'analyse de la voix humaine et ses contraintes en vous présentant les rouages internes du logiciel libre "Recognito", créé par le conférencier et permettant l'identification d'un locuteur à partir de son empreinte vocale.
Text independent speaker recognition systemDeepesh Lekhak
This document outlines a project to develop a text-independent speaker recognition system. It lists the project members and provides an overview of the presentation sections, which include the system architecture, methodology, results and analysis, and applications. The methodology section describes implementing the system in MATLAB, including voice capturing, pre-processing, MFCC feature extraction, GMM matching, and identification/verification. It also outlines implementing the system on an FPGA, including analog conversion, storage, framing, FFT, mel spectrum, MFCC extraction, and UART transmission to MATLAB for further processing. The results show over 99% recognition accuracy with longer training and test data.
iVector vs GMM/UBM for Automatic Speaker Recognition system Walid Bouaffou
Ce travail s’inscrit dans le domaine de la Reconnaissance Automatique du Locuteur (RAL) dont l’objectif est de reconnaitre une personne en analysant sa voix en conditions réelles.
Le système développé dans le cadre de ce projet repose sur l’utilisation des MFCC (Mel Frequency
Cepstral Coefficients) comme paramètres caractéristiques de la voix et l’approche GMM-UBM (Gaussian Mixture Models- Universal Background Model) comme une première phase de modélisation. La deuxième phase consiste à projeter les modèles GMM dans un espace réduit qui englobe la variabilité du locuteur et celle du canal communément appelé espace de variabilité totale. Les modèles résultants de cette projection s’appellent i-vecteurs et ils représentent les identités des locuteurs clients. La dernière phase de cette modélisation consiste à éliminer les composantes indésirables dues essentiellement aux effets du canal à travers l’application de certaines techniques d’analyse factorielle.
L’évaluation des performances de la modélisation par i-vecteurs a été effectuée en comparant ses résultats en termes de temps de réponse, de complexité et de taux de reconnaissance, avec ceux obtenus en utilisant la modélisation classique GMM-UBM.
This
Text Prompted Remote Speaker Authentication : Joint Speech and Speaker Recogn...gt_ebuddy
Joint Speech and Speaker Recognition using Hidden Markov Model/Vector Quantization for speaker independent Speech Recognition and Gaussian Mixture Model for speech independent speaker recognition- used MFCC (Mel-Frequency Cepstral Coefficient) for Feature Extraction (delta,delta delta and energy - 39 coefficients).
Developed in JAVA with client/server Architecture, web interface developed in Adobe Flex.
This project was done at TU, IOE - Pulchowk Campus, Nepal.
For more details visit http://ganeshtiwaridotcomdotnp.blogspot.com
ABSTRACT OF PROJECT>>>
Biometric is physical characteristic unique to each individual. It has a very useful application in authentication and access control.
The designed system is a text-prompted version of voice biometric which incorporates text-independent speaker verification and speaker-independent speech verification system implemented independently. The foundation for this joint system is that the speech signal conveys both the speech content and speaker identity. Such systems are more-secure from playback attack, since the word to speak during authentication is not previously set.
During the course of the project various digital signal processing and pattern classification algorithms were studied. Short time spectral analysis was performed to obtain MFCC, energy and their deltas as feature. Feature extraction module is same for both systems. Speaker modeling was done by GMM and Left to Right Discrete HMM with VQ was used for isolated word modeling. And results of both systems were combined to authenticate the user.
The speech model for each word was pre-trained by using utterance of 45 English words. The speaker model was trained by utterance of about 2 minutes each by 15 speakers. While uttering the individual words, the recognition rate of the speech recognition system is 92 % and speaker recognition system is 66%. For longer duration of utterance (>5sec) the recognition rate of speaker recognition system improves to 78%.
This document summarizes a presentation on baseline speaker verification. It describes preprocessing speech signals using voice activity detection, extracting mel-frequency cepstral coefficients as features, building Gaussian mixture models during enrollment and testing phases, and evaluating performance using equal error rates. The authors achieved their best performance with 64 Gaussian components when both training and testing data were full utterances. Future work includes data augmentation and validating results using i-vector modeling.
This document describes how to build a simple automatic speaker recognition system. It discusses the principles of speaker recognition, which can be identification (determining which registered speaker is speaking) or verification (accepting or rejecting a speaker's claimed identity). The key components are feature extraction and feature matching. Feature extraction converts the speech waveform into features using techniques like MFCC. Feature matching then compares the extracted features to stored reference models to identify the speaker. The document focuses on the speech feature extraction process, which involves framing the speech signal, windowing frames, taking the FFT, and calculating MFCCs to characterize the signal in a way that mimics human hearing.
Text Prompted Remote Speaker Authentication : Joint Speech and Speaker Recogn...gt_ebuddy
Joint Speech and Speaker Recognition using Hidden Markov Model/Vector Quantization for speaker independent Speech Recognition and Gaussian Mixture Model for speech independent speaker recognition- used MFCC (Mel-Frequency Cepstral Coefficient) for Feature Extraction (delta,delta delta and energy - 39 coefficients).
Developed in JAVA with client/server Architecture, web interface developed in Adobe Flex.
This project was done at TU, IOE - Pulchowk Campus, Nepal.
For more details visit http://ganeshtiwaridotcomdotnp.blogspot.com
ABSTRACT OF PROJECT>>>
Biometric is physical characteristic unique to each individual. It has a very useful application in authentication and access control.
The designed system is a text-prompted version of voice biometric which incorporates text-independent speaker verification and speaker-independent speech verification system implemented independently. The foundation for this joint system is that the speech signal conveys both the speech content and speaker identity. Such systems are more-secure from playback attack, since the word to speak during authentication is not previously set.
During the course of the project various digital signal processing and pattern classification algorithms were studied. Short time spectral analysis was performed to obtain MFCC, energy and their deltas as feature. Feature extraction module is same for both systems. Speaker modeling was done by GMM and Left to Right Discrete HMM with VQ was used for isolated word modeling. And results of both systems were combined to authenticate the user.
The speech model for each word was pre-trained by using utterance of 45 English words. The speaker model was trained by utterance of about 2 minutes each by 15 speakers. While uttering the individual words, the recognition rate of the speech recognition system is 92 % and speaker recognition system is 66%. For longer duration of utterance (>5sec) the recognition rate of speaker recognition system improves to 78%.
Business Process Execution Language (ou BPEL, prononcé « bipeul », ou « bipèl »), est un langage de programmation destiné à l'exécution des procédures d'entreprise. Le BPEL est issu des langages WSFL (Web Services Flow Language) et XLANG, et est dérivé du XML.
Neurosciences et spiritualité fr_ Nancy -20131110jlroux
Quant neurosciences et spiritualité nous amènent à l’Etre
Dans cette conférence, Jean-Luc Roux présentera l’approche neuro-scientifque en tant que démarche de connaissance de soi et de référence sur le chemin spirituel. Cette approche consiste en une compréhension de nos quatre cerveaux directement liés à nos états d’être (bien aise ou malaise), nos relations aux autres, notre positionnement en société, nos personnalités (talents ou compétences). La connaissance de ces outils peuvent faciliter l’adaptation au changement dans un monde en crise.
L’orateur vous proposera un voyage passionnant dans nos territoires cérébraux. Il passera en revue l’apport de chaque cerveau dans nos décisions et la joie intérieure à laquelle ils peuvent mener lorsque nous restons à leur écoute.
Le dernier né de notre évolution est le cerveau pré-frontal ou néocortex. C’est la que réside notre intelligence émotionnelle. Il est silencieux et ne peut agir que si toutes les autres structures sont calmes. Nous comprendrons alors le rôle de la méditation, de la pleine conscience, du chant, de la danse et tous les arts. C'est la dimension transcendante de l'Etre.
The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.
Expectation Maximization and Gaussian Mixture Modelspetitegeek
Here are some other potential applications of EM:
- EM can be used for parameter estimation in hidden Markov models (HMMs). The hidden states are the latent variables estimated using EM.
- EM can be used for topic modeling using latent Dirichlet allocation (LDA). The topics are the latent variables estimated from documents.
- As mentioned in the document, EM can also be used for Gaussian mixture models (GMMs) for clustering and density estimation. The cluster assignments are latent.
- EM can be used for missing data problems, where the missing values are treated as latent variables estimated each iteration.
- Bayesian networks and directed graphical models more generally can also be estimated using EM by treating the conditional probabilities as latent
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
This document discusses machine learning techniques including k-means clustering, expectation maximization (EM), and Gaussian mixture models (GMM). It begins by introducing unsupervised learning problems and k-means clustering. It then describes EM as a general algorithm for maximum likelihood estimation and density estimation. Finally, it discusses using GMM with EM to model data distributions and for classification tasks.
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice and perform tasks. It uses acoustic and language models to recognize speech. Acoustic models are statistical representations of sounds created from audio recordings and transcriptions, while language models predict word sequences. There are two main types: speaker-dependent systems require user training to recognize individual voices more accurately, while speaker-independent systems used in applications like phones do not require training but are generally less accurate. The speech recognition process involves digitizing speech, analyzing acoustic signals, and linguistically interpreting the speech to recognize words.
This document summarizes a speech recognition system (SRS). SRS uses speech identification and verification. Speech identification determines which registered speaker provided an utterance by extracting features like mel-frequency cepstrum coefficients and comparing them. Speech verification accepts or rejects an identity claim by clustering training vectors from an enrollment session into speaker-specific codebooks using vector quantization. Applications of SRS include banking by phone, voice dialing, voice mail, and security control.
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
Présentation générale de ce que sont les métadonnées, de quelques questions qu'elles soulèvent, suivie d'une proposition de typologie des standards de métadonnées.
Manquent les animations
Version 1.1
"Le traitement automatique du langage (TAL) face aux données textuelles volumineuses et potentiellement dégradées : qu’est-ce que cela change ?" : Présentation de Pascale Sebillot, chercheuse à l'IRISA lors du séminaire IST Inria : "Big Data, nouvelles partitions de l'information" ; Saint-Paul-Lès-Dax du 6 au 10 octobre 2014.
Text independent speaker recognition systemDeepesh Lekhak
This document outlines a project to develop a text-independent speaker recognition system. It lists the project members and provides an overview of the presentation sections, which include the system architecture, methodology, results and analysis, and applications. The methodology section describes implementing the system in MATLAB, including voice capturing, pre-processing, MFCC feature extraction, GMM matching, and identification/verification. It also outlines implementing the system on an FPGA, including analog conversion, storage, framing, FFT, mel spectrum, MFCC extraction, and UART transmission to MATLAB for further processing. The results show over 99% recognition accuracy with longer training and test data.
iVector vs GMM/UBM for Automatic Speaker Recognition system Walid Bouaffou
Ce travail s’inscrit dans le domaine de la Reconnaissance Automatique du Locuteur (RAL) dont l’objectif est de reconnaitre une personne en analysant sa voix en conditions réelles.
Le système développé dans le cadre de ce projet repose sur l’utilisation des MFCC (Mel Frequency
Cepstral Coefficients) comme paramètres caractéristiques de la voix et l’approche GMM-UBM (Gaussian Mixture Models- Universal Background Model) comme une première phase de modélisation. La deuxième phase consiste à projeter les modèles GMM dans un espace réduit qui englobe la variabilité du locuteur et celle du canal communément appelé espace de variabilité totale. Les modèles résultants de cette projection s’appellent i-vecteurs et ils représentent les identités des locuteurs clients. La dernière phase de cette modélisation consiste à éliminer les composantes indésirables dues essentiellement aux effets du canal à travers l’application de certaines techniques d’analyse factorielle.
L’évaluation des performances de la modélisation par i-vecteurs a été effectuée en comparant ses résultats en termes de temps de réponse, de complexité et de taux de reconnaissance, avec ceux obtenus en utilisant la modélisation classique GMM-UBM.
This
Text Prompted Remote Speaker Authentication : Joint Speech and Speaker Recogn...gt_ebuddy
Joint Speech and Speaker Recognition using Hidden Markov Model/Vector Quantization for speaker independent Speech Recognition and Gaussian Mixture Model for speech independent speaker recognition- used MFCC (Mel-Frequency Cepstral Coefficient) for Feature Extraction (delta,delta delta and energy - 39 coefficients).
Developed in JAVA with client/server Architecture, web interface developed in Adobe Flex.
This project was done at TU, IOE - Pulchowk Campus, Nepal.
For more details visit http://ganeshtiwaridotcomdotnp.blogspot.com
ABSTRACT OF PROJECT>>>
Biometric is physical characteristic unique to each individual. It has a very useful application in authentication and access control.
The designed system is a text-prompted version of voice biometric which incorporates text-independent speaker verification and speaker-independent speech verification system implemented independently. The foundation for this joint system is that the speech signal conveys both the speech content and speaker identity. Such systems are more-secure from playback attack, since the word to speak during authentication is not previously set.
During the course of the project various digital signal processing and pattern classification algorithms were studied. Short time spectral analysis was performed to obtain MFCC, energy and their deltas as feature. Feature extraction module is same for both systems. Speaker modeling was done by GMM and Left to Right Discrete HMM with VQ was used for isolated word modeling. And results of both systems were combined to authenticate the user.
The speech model for each word was pre-trained by using utterance of 45 English words. The speaker model was trained by utterance of about 2 minutes each by 15 speakers. While uttering the individual words, the recognition rate of the speech recognition system is 92 % and speaker recognition system is 66%. For longer duration of utterance (>5sec) the recognition rate of speaker recognition system improves to 78%.
This document summarizes a presentation on baseline speaker verification. It describes preprocessing speech signals using voice activity detection, extracting mel-frequency cepstral coefficients as features, building Gaussian mixture models during enrollment and testing phases, and evaluating performance using equal error rates. The authors achieved their best performance with 64 Gaussian components when both training and testing data were full utterances. Future work includes data augmentation and validating results using i-vector modeling.
This document describes how to build a simple automatic speaker recognition system. It discusses the principles of speaker recognition, which can be identification (determining which registered speaker is speaking) or verification (accepting or rejecting a speaker's claimed identity). The key components are feature extraction and feature matching. Feature extraction converts the speech waveform into features using techniques like MFCC. Feature matching then compares the extracted features to stored reference models to identify the speaker. The document focuses on the speech feature extraction process, which involves framing the speech signal, windowing frames, taking the FFT, and calculating MFCCs to characterize the signal in a way that mimics human hearing.
Text Prompted Remote Speaker Authentication : Joint Speech and Speaker Recogn...gt_ebuddy
Joint Speech and Speaker Recognition using Hidden Markov Model/Vector Quantization for speaker independent Speech Recognition and Gaussian Mixture Model for speech independent speaker recognition- used MFCC (Mel-Frequency Cepstral Coefficient) for Feature Extraction (delta,delta delta and energy - 39 coefficients).
Developed in JAVA with client/server Architecture, web interface developed in Adobe Flex.
This project was done at TU, IOE - Pulchowk Campus, Nepal.
For more details visit http://ganeshtiwaridotcomdotnp.blogspot.com
ABSTRACT OF PROJECT>>>
Biometric is physical characteristic unique to each individual. It has a very useful application in authentication and access control.
The designed system is a text-prompted version of voice biometric which incorporates text-independent speaker verification and speaker-independent speech verification system implemented independently. The foundation for this joint system is that the speech signal conveys both the speech content and speaker identity. Such systems are more-secure from playback attack, since the word to speak during authentication is not previously set.
During the course of the project various digital signal processing and pattern classification algorithms were studied. Short time spectral analysis was performed to obtain MFCC, energy and their deltas as feature. Feature extraction module is same for both systems. Speaker modeling was done by GMM and Left to Right Discrete HMM with VQ was used for isolated word modeling. And results of both systems were combined to authenticate the user.
The speech model for each word was pre-trained by using utterance of 45 English words. The speaker model was trained by utterance of about 2 minutes each by 15 speakers. While uttering the individual words, the recognition rate of the speech recognition system is 92 % and speaker recognition system is 66%. For longer duration of utterance (>5sec) the recognition rate of speaker recognition system improves to 78%.
Business Process Execution Language (ou BPEL, prononcé « bipeul », ou « bipèl »), est un langage de programmation destiné à l'exécution des procédures d'entreprise. Le BPEL est issu des langages WSFL (Web Services Flow Language) et XLANG, et est dérivé du XML.
Neurosciences et spiritualité fr_ Nancy -20131110jlroux
Quant neurosciences et spiritualité nous amènent à l’Etre
Dans cette conférence, Jean-Luc Roux présentera l’approche neuro-scientifque en tant que démarche de connaissance de soi et de référence sur le chemin spirituel. Cette approche consiste en une compréhension de nos quatre cerveaux directement liés à nos états d’être (bien aise ou malaise), nos relations aux autres, notre positionnement en société, nos personnalités (talents ou compétences). La connaissance de ces outils peuvent faciliter l’adaptation au changement dans un monde en crise.
L’orateur vous proposera un voyage passionnant dans nos territoires cérébraux. Il passera en revue l’apport de chaque cerveau dans nos décisions et la joie intérieure à laquelle ils peuvent mener lorsque nous restons à leur écoute.
Le dernier né de notre évolution est le cerveau pré-frontal ou néocortex. C’est la que réside notre intelligence émotionnelle. Il est silencieux et ne peut agir que si toutes les autres structures sont calmes. Nous comprendrons alors le rôle de la méditation, de la pleine conscience, du chant, de la danse et tous les arts. C'est la dimension transcendante de l'Etre.
The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.
Expectation Maximization and Gaussian Mixture Modelspetitegeek
Here are some other potential applications of EM:
- EM can be used for parameter estimation in hidden Markov models (HMMs). The hidden states are the latent variables estimated using EM.
- EM can be used for topic modeling using latent Dirichlet allocation (LDA). The topics are the latent variables estimated from documents.
- As mentioned in the document, EM can also be used for Gaussian mixture models (GMMs) for clustering and density estimation. The cluster assignments are latent.
- EM can be used for missing data problems, where the missing values are treated as latent variables estimated each iteration.
- Bayesian networks and directed graphical models more generally can also be estimated using EM by treating the conditional probabilities as latent
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
This document discusses machine learning techniques including k-means clustering, expectation maximization (EM), and Gaussian mixture models (GMM). It begins by introducing unsupervised learning problems and k-means clustering. It then describes EM as a general algorithm for maximum likelihood estimation and density estimation. Finally, it discusses using GMM with EM to model data distributions and for classification tasks.
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice and perform tasks. It uses acoustic and language models to recognize speech. Acoustic models are statistical representations of sounds created from audio recordings and transcriptions, while language models predict word sequences. There are two main types: speaker-dependent systems require user training to recognize individual voices more accurately, while speaker-independent systems used in applications like phones do not require training but are generally less accurate. The speech recognition process involves digitizing speech, analyzing acoustic signals, and linguistically interpreting the speech to recognize words.
This document summarizes a speech recognition system (SRS). SRS uses speech identification and verification. Speech identification determines which registered speaker provided an utterance by extracting features like mel-frequency cepstrum coefficients and comparing them. Speech verification accepts or rejects an identity claim by clustering training vectors from an enrollment session into speaker-specific codebooks using vector quantization. Applications of SRS include banking by phone, voice dialing, voice mail, and security control.
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
Présentation générale de ce que sont les métadonnées, de quelques questions qu'elles soulèvent, suivie d'une proposition de typologie des standards de métadonnées.
Manquent les animations
Version 1.1
"Le traitement automatique du langage (TAL) face aux données textuelles volumineuses et potentiellement dégradées : qu’est-ce que cela change ?" : Présentation de Pascale Sebillot, chercheuse à l'IRISA lors du séminaire IST Inria : "Big Data, nouvelles partitions de l'information" ; Saint-Paul-Lès-Dax du 6 au 10 octobre 2014.
L'empreinte audio numerique au service de l'analyse des diffusions Masterclas...ACTUONDA
L’Empreinte Audio Numérique au service de l’analyse des diffusions
Solutions de reconnaissance automatique de contenu
Masterclass OPNS au Salon de la Radio et de l'Audio Digital 2019
Introduction sur les domaines scientifiques impliqués dans la fouille de textes
- TAL et fouille de données : En quoi les données textuelles sont particulières (lexique, syntaxe mais aussi diversité langagière, des formats, des entités, des méta-données etc.) et quels sont les types de ressources utiles ou disponibles.
- Des modèles et des tâches (analyse grammaticale, désambiguisation, similarité textuelle, recherche et extraction d'information, classification...) et des collections standard pour évaluer des modèles et des outils
- Les approches automatisées sont associées à différentes manières de travailler les corpus (règles manuelles, apprentissage et bases d'exemples, degrés de supervision humaine, ...) : avantages / inconvénients, risques ...
Panorama méthodologique de l'offre logicielle académique ou commerciale
- Des outils pour l'utilisateur final, des APIs pour le développement, des plateformes d'annotation pour la création de bases d'apprentissage, des outils pour écrire des règles symboliques
- Des outils logiciels plus ou moins interactifs
Présentation effectuée à la création numérique "Alchimie 13" par Christophe Villeneuve sur "La voix avec Common Voice".
Vous allez voir l'avancé de ces appareils, des projets libres comme Common Voice et DeepSpeech, la qualité des participations et contributions
Consulter le site officiel https://voice.mozilla.org
Cours sur les normes et standards (principales notions à connaître) auprès d'étudiants en Master Multimédia de Bordeaux. Principaux enjeux des normes et standards abordés : accessibilité, indexation (avec métadonnées et dublin core), interopérabilité, web sémantique et open data.
The document discusses video surveillance technologies for security applications. It provides an overview of common application domains for video surveillance such as transportation, public events, and industrial environments. It also discusses the need for "smart video surveillance" capabilities due to the large amount of video data and limitations of human monitoring. Key functionalities of intelligent video surveillance systems include detection of intrusions, loitering, counting people and vehicles, and recognizing vehicles, license plates, and people.
The document discusses handling variability from design-time to runtime in dynamic adaptive systems. It introduces the concept of hyper-agility, which extends agile principles to runtime. Model-driven engineering and aspect-oriented techniques can be used to model variability and dynamically reconfigure systems. Variants are modeled as aspects that can be woven into a base model. Runtime validation is needed due to the large number of possible configurations from combining aspects. Reconfiguration scripts can be automatically generated from models to dynamically adapt systems.
1. Les enjeux scientifiques de l’indexation vidéo Patrick Gros Responsable de l’équipe TEXMEX INRIA Rennes et IRISA http://www.irisa.fr/texmex
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19. Web-based topic adaptation ... ... € … thus a candidate who fails to carry a particular state receives not a single electoral vote in that state for the popular votes received since residential elections are won by electoral ... candidate state election 3. Building of an adaptation corpus candidate vote electoral vote 2. Querying 1. Keyword spotting Adaptation LM 4.a Training of a topic-specific LM 4.b Mix of this LM and the general one Baseline LM + Adapted LM = Web search engine ✘ ✔ ✔ ✔ ✘ ✔ ✘ ✔