CUNI at MediaEval 2013 Similar Segments in Social Speech Task
•
1 like•961 views
This document summarizes an approach for identifying similar segments in social speech using machine learning segmentation techniques. It discusses: 1) Creating queries from human transcripts and indexing recordings using an IR platform after preprocessing. 2) Segmenting recordings regularly into overlapping passages or using machine learning classification trees trained on human transcripts to identify segment boundaries. 3) Features and models used for the machine learning segmentation of beginnings and ends of segments. 4) Evaluation results showing regular segmentation on ASR transcripts achieved the overall best performance.
Report
Share
Report
Share
Download to read offline
Recommended
Natural Language processing Parts of speech tagging, its classes, and how to ...
Part of speech (POS) tagging is the process of assigning a part of speech tag like noun, verb, adjective to each word in a sentence. It involves determining the most likely tag sequence given the probabilities of tags occurring before or after other tags, and words occurring with certain tags. POS tagging is the first step in many NLP applications and helps determine the grammatical role of words. It involves calculating bigram and lexical probabilities from annotated corpora to find the tag sequence with the highest joint probability.
Parts of Speect Tagging
The document discusses parts-of-speech (POS) tagging. It defines POS tagging as labeling each word in a sentence with its appropriate part of speech. It provides an example tagged sentence and discusses the challenges of POS tagging, including ambiguity and open/closed word classes. It also discusses common tag sets and stochastic POS tagging using hidden Markov models.
Seminar
1. Dokumen tersebut membahas tentang kesadaran masyarakat terhadap penanganan sampah di Kota Palu dan hak serta kewajiban masyarakat berdasarkan UU No. 18 Tahun 2008.
2. Kasus di Kota Palu menunjukkan bahwa volume sampah yang dihasilkan lebih besar dari yang dapat diangkut, menimbulkan permasalahan lingkungan.
3. UU No. 18 Tahun 2008 mengatur hak masyarakat untuk mendap
Q3
Universal Pictures, Studio Canal, and Working Title Pictures distributed the romantic comedy film Love Actually because they see potential in funding romantic comedies as a genre. The leading producers and distributors of romantic comedies include Disney, MGM, and Sony, whose films in the genre have grossed around half a billion pounds each. While Disney would be an obvious choice due to their success distributing romantic comedies, their child-oriented market makes it unlikely they would distribute a film with dark humor. Therefore, the author would choose Universal or MGM as distributors, as they have funded R-rated romantic comedies like What Women Want, making them a better fit for the author's film.
Unit01 bac
1) The document describes two experiments involving oxidation-reduction reactions. The first experiment measured the rate of decomposition of hydrogen peroxide over time. The second experiment measured the rate of a redox reaction between potassium iodide and persulfate ions.
2) Both experiments involved measuring the volume of gas produced over time to determine the amount of reactant consumed and the rate of reaction. Rate calculations were performed using the volume and time data.
3) Thermodynamic considerations and balanced chemical equations were provided to explain the redox reactions occurring in each experiment.
Recommended
Natural Language processing Parts of speech tagging, its classes, and how to ...
Part of speech (POS) tagging is the process of assigning a part of speech tag like noun, verb, adjective to each word in a sentence. It involves determining the most likely tag sequence given the probabilities of tags occurring before or after other tags, and words occurring with certain tags. POS tagging is the first step in many NLP applications and helps determine the grammatical role of words. It involves calculating bigram and lexical probabilities from annotated corpora to find the tag sequence with the highest joint probability.
Parts of Speect Tagging
The document discusses parts-of-speech (POS) tagging. It defines POS tagging as labeling each word in a sentence with its appropriate part of speech. It provides an example tagged sentence and discusses the challenges of POS tagging, including ambiguity and open/closed word classes. It also discusses common tag sets and stochastic POS tagging using hidden Markov models.
Seminar
1. Dokumen tersebut membahas tentang kesadaran masyarakat terhadap penanganan sampah di Kota Palu dan hak serta kewajiban masyarakat berdasarkan UU No. 18 Tahun 2008.
2. Kasus di Kota Palu menunjukkan bahwa volume sampah yang dihasilkan lebih besar dari yang dapat diangkut, menimbulkan permasalahan lingkungan.
3. UU No. 18 Tahun 2008 mengatur hak masyarakat untuk mendap
Q3
Universal Pictures, Studio Canal, and Working Title Pictures distributed the romantic comedy film Love Actually because they see potential in funding romantic comedies as a genre. The leading producers and distributors of romantic comedies include Disney, MGM, and Sony, whose films in the genre have grossed around half a billion pounds each. While Disney would be an obvious choice due to their success distributing romantic comedies, their child-oriented market makes it unlikely they would distribute a film with dark humor. Therefore, the author would choose Universal or MGM as distributors, as they have funded R-rated romantic comedies like What Women Want, making them a better fit for the author's film.
Unit01 bac
1) The document describes two experiments involving oxidation-reduction reactions. The first experiment measured the rate of decomposition of hydrogen peroxide over time. The second experiment measured the rate of a redox reaction between potassium iodide and persulfate ions.
2) Both experiments involved measuring the volume of gas produced over time to determine the amount of reactant consumed and the rate of reaction. Rate calculations were performed using the volume and time data.
3) Thermodynamic considerations and balanced chemical equations were provided to explain the redox reactions occurring in each experiment.
L06 stemmer and edit distance
Discusses the edit distance concepts, applications and the algorithms to compute the minimum edit distance with an example.
Experiments with Segmentation Strategies for Passage Retrieval in Audio-Visua...
This document summarizes research on using different segmentation strategies for passage retrieval in audio-visual documents. It describes experiments comparing regular window-based segmentation to feature-based segmentation using machine learning. Feature-based segmentation outperformed regular segmentation on two benchmark tasks, retrieving similar segments from social speech interviews and searching/hyperlinking TV programs. While results were clearer for the interview data, some feature-based approaches also showed promise for the TV program data when applied to subtitles. Overall, the research suggests feature-based segmentation can improve passage retrieval over simple windowing approaches.
CUNI at MediaEval 2012: Search and Hyperlinking Task
The document summarizes CUNI's participation in the Search and Hyperlinking Task at MediaEval 2012. It describes their use of two transcription files, two segmentation strategies (regular time-based and TextTiling), and the Terrier information retrieval system. Their highest scores for MRR and mGAP measures used regular 90-second segments, while TextTiling achieved the highest MASP scores. Shorter regular segments performed better for mGAP and MASP, while MRR peaked at 90 seconds. Future work could improve segmentation precision and incorporate audio/visual features.
Integration of speech recognition with computer assisted translation
This document discusses the integration of speech recognition with computer-assisted translation. It begins by introducing machine translation and computer-assisted translation, then describes how automatic speech recognition works and how it can be integrated with translation. Key approaches to integration include using word graphs from ASR and MT systems or rescoring ASR hypotheses with translation models. Neural machine translation models that use attention mechanisms are also discussed. The document concludes by noting areas for further development in reducing human effort in translation and increasing quality and effectiveness of speech-to-text and translation tools.
STREAMING PUNCTUATION: A NOVEL PUNCTUATION TECHNIQUE LEVERAGING BIDIRECTIONAL...
The document proposes a streaming punctuation technique that leverages bidirectional context for continuous speech recognition. It introduces a novel approach of streaming punctuation that discards decoder segmentation and shifts punctuation decision making to a powerful Transformer model. Experimental results show that streaming punctuation improves segmentation accuracy by 13.9% and achieves an average BLEU score gain of 0.66 for downstream machine translation tasks.
Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional...
While speech recognition Word Error Rate (WER) has reached human parity for English, continuous speech recognition scenarios such as voice typing and meeting transcriptions still suffer from segmentation and punctuation problems, resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions. Context within the segments produced by ASR decoders can be helpful but limiting in overall punctuation performance for a continuous speech session. In this paper, we propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEUscore improvement of 0.66 for the downstream task of Machine Translation (MT).
third_seminar
The document discusses developing a text-to-speech (TTS) system that can synthesize speech in a storytelling style from written story text. It aims to 1) develop a story TTS using a neutral TTS system and incorporating story-specific information, 2) develop a story TTS using a story speech corpus, and 3) model prosody patterns including pauses for story-style speech. The work involves building modules for story-specific emotion detection, prosody generation and incorporation. Models are developed to predict pause position and duration using rule-based, supervised and unsupervised methods. A multi-model framework improves pause duration prediction over a single model.
Ch09 combinatorialpatternmatching
This document discusses genomic repeats and techniques for finding repeats and matching patterns in DNA sequences. It covers using hash tables to store and retrieve DNA sequences, building keyword trees to represent sets of sequences, and constructing suffix trees to efficiently find all occurrences of a pattern in a string. Suffix trees allow pattern matching to be performed in linear time compared to quadratic time for a naive approach. The document also introduces the problem of approximate pattern matching and heuristic search algorithms used to quickly find similar matches in large genomes.
Reference Scope Identification in Citing Sentences
This document describes an approach to identify the scope of references in sentences containing multiple citations. The approach uses a conditional random field (CRF) sequence model with features including distance, position, part-of-speech tags, and syntactic dependencies. It first segments sentences using punctuation and conjunctions, then applies the CRF and a majority voting scheme to label each word as inside or outside the scope. The method achieved over 90% accuracy on a dataset of citations from ACL papers.
Vladyslav Hamolia "How to choose ASR (automatic speech recognition) system"
This document provides an overview of automatic speech recognition (ASR) technologies. It discusses the history and challenges of ASR, the general processing flow including speech representation techniques like MFCCs and acoustic and language modeling using HMMs and DNNs. It also introduces some open source ASR tools like CMU Sphinx and Kaldi and provides some ASR demonstration results on the TIMIT dataset using a BiLSTM model. The document concludes with questions for discussion.
ICCIT_presentation
The document presents a methodology for isolated Bangla word recognition and speaker detection using a Semantic Modular Time Delay Neural Network (MTDNN). The authors collected a dataset of 690 audio samples from 5 speakers uttering 46 Bangla words. They used the MTDNN framework to classify words and identify speakers with an accuracy of 82.66%. This represents an improvement over previous work on Bangla speech recognition. The authors conclude the MTDNN approach is effective for Bangla word recognition and speaker detection but recommend expanding the dataset and vocabulary in future work.
Sequence to sequence model speech recognition
The document discusses sequence to sequence models for speech recognition. It describes how traditional automatic speech recognition (ASR) works using acoustic, pronunciation, and language models. The document then introduces sequence to sequence models like Listen, Attend and Spell (LAS) which uses an encoder, attender, and decoder. LAS improves upon traditional ASR by integrating all models into a single neural network with attention and other optimizations like minimum word error rate training and scheduled sampling. Sequence to sequence models provide around 11% relative improvement in word error rate over traditional ASR systems.
nakai22apsipa_presentation.pdf
This document describes a multi-task adversarial training algorithm for improving the performance of multi-speaker neural text-to-speech (TTS) models, especially for voice cloning of unseen speakers. The proposed method augments GANSpeech training with an autoencoder interpolation regularization technique to diversify speaker variations during training. Evaluation shows the method generates higher quality synthesized speech for seen speakers compared to baselines, and also improves voice cloning for unseen speakers by reducing quality degradation. However, there is still a gap in speaker similarity between seen and unseen speakers, indicating room for further improvement.
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
This document summarizes a tutorial for developing a state-of-the-art named entity recognition framework using deep learning. The tutorial uses a bi-directional LSTM-CNN architecture with a CRF layer, as presented in a 2016 paper. It replicates the paper's results on the CoNLL 2003 dataset for NER, achieving an F1 score of 91.21. The tutorial covers data preparation from the dataset, word embeddings using GloVe vectors, a CNN encoder for character-level representations, a bi-LSTM for word-level encoding, and a CRF layer for output decoding and sequence tagging. The experience of presenting this tutorial to friends highlighted the need for detailed comments and explanations of each step and PyTorch functions.
Introducing data analysis: reads to results
Some reads could align to multiple locations:
Reference: AGTCTTAGGGACTTTATAC
AGTC TAGG
TTAC CTTT
GGGA
This is ambiguous - TTAC and GGGA could align in two places each.
We need more information (longer reads or paired reads) to resolve.
NLP Asignment Final Presentation [IIT-Bombay]
The final presentation I did with Lekha & Deepali for the Natural Language Processing assignments at IIT-Bombay.
Assignments included:
1: Spelling Correction
2: Part-of-speech Tagging
3: Metaphor Detection
BITS - Comparative genomics: the Contra tool
This is the last presentation of the BITS training on 'Comparative genomics'.
It reviews tthe Contra tool for detecting common transcription factor binding sites in sequences.
Thanks to Stefan Broos of the DMBR department of VIB
Multimodal Features for Search and Hyperlinking of Video Content
In the talk, I will discuss content-based retrieval in audio-visual collections. I will focus on retrieval of relevant segments of video using a textual query. In addition, I will describe techniques for detecting hyperlinks within audio-visual collections. Our retrieval system ranked first in the MediaEval 2014 Search and Hyperlinking shared task. The experiments were performed on almost 4000 hours of BBC broadcast video.
As the segmentation of the recordings shows to be crucial for high-quality video retrieval and hyperlinking, I will focus on segmentation strategies. I will show the possibility of employment of the prosodic and visual information into the segmentation process. Our decision tree-based segmentation proved to outperform fixed-length segmentation which regularly achieves the best results in the retrieval process. Visual and prosodic similarity are also explored in addition to the hyperlinking based on the subtitles and automatic transcripts. The employment of the visual similarity achieves a constant improvement, while the employment of the prosodic similarity shows a small but promising improvement too.
lec26_audio.pptx
This document discusses deep learning applications for audio, including speech recognition and text-to-speech. It outlines several deep learning models for automatic speech recognition, including DNN-HMM models that use deep neural networks for frame classification in HMM-based speech recognition systems. Recurrent neural networks like LSTMs are also mentioned for speech recognition. The document also briefly discusses text-to-speech models like WaveNet and Tacotron that generate speech from text using neural networks.
Filling the gaps
The slides present a text recovery method based on a probabilistic post-recognition processing of the output of an Optical Character Recognition system. The proposed method is trying to fill in the gaps of missing text resulted from the recognition process of degraded documents. For this task, a corpus of up to 5-grams provided by Google is used. Several heuristics for using this corpus for the fulfilment of this task are described after presenting the general problem and alternative solutions. These heuristics have been validated using a set of experiments that are also discussed together with the results that have been obtained.
Combining Evidence for Cross-language Information Retrieval
System combination has been extensively studied in monolingual information retrieval, but the problem is understudied in cross-language retrieval in which queries are expressed in one language, but documents are written in another. One notable characteristic of cross-language retrieval, however, is the potential for a greater diversity of system design, since translation and retrieval components both exhibit substantial design spaces. Due to the large diversity of the systems in cross-language retrieval, the potential range of combinations is orders of magnitude larger than in monolingual applications.
I show that evidence combination works well in cross-language retrieval, achieving improvements of 40% relative to the best single system. The best results are obtained using post-retrieval evidence combination, which is able to incorporate many diverse high-quality systems. Because hundreds of different systems can be built, the effectiveness of alternative approaches for managing the complexity is also explored. Both system clustering and expert judgment regarding diversity can help to limit the combinatorial growth of time complexity arising when selections among large numbers of systems need to be made.
Multimodal Features for Linking Television Content
How can acoustic and visual features be combined with text-based search methods applied on automatic transcripts and subtitles and help to retrieve television content.
More Related Content
Similar to CUNI at MediaEval 2013 Similar Segments in Social Speech Task
L06 stemmer and edit distance
Discusses the edit distance concepts, applications and the algorithms to compute the minimum edit distance with an example.
Experiments with Segmentation Strategies for Passage Retrieval in Audio-Visua...
This document summarizes research on using different segmentation strategies for passage retrieval in audio-visual documents. It describes experiments comparing regular window-based segmentation to feature-based segmentation using machine learning. Feature-based segmentation outperformed regular segmentation on two benchmark tasks, retrieving similar segments from social speech interviews and searching/hyperlinking TV programs. While results were clearer for the interview data, some feature-based approaches also showed promise for the TV program data when applied to subtitles. Overall, the research suggests feature-based segmentation can improve passage retrieval over simple windowing approaches.
CUNI at MediaEval 2012: Search and Hyperlinking Task
The document summarizes CUNI's participation in the Search and Hyperlinking Task at MediaEval 2012. It describes their use of two transcription files, two segmentation strategies (regular time-based and TextTiling), and the Terrier information retrieval system. Their highest scores for MRR and mGAP measures used regular 90-second segments, while TextTiling achieved the highest MASP scores. Shorter regular segments performed better for mGAP and MASP, while MRR peaked at 90 seconds. Future work could improve segmentation precision and incorporate audio/visual features.
Integration of speech recognition with computer assisted translation
This document discusses the integration of speech recognition with computer-assisted translation. It begins by introducing machine translation and computer-assisted translation, then describes how automatic speech recognition works and how it can be integrated with translation. Key approaches to integration include using word graphs from ASR and MT systems or rescoring ASR hypotheses with translation models. Neural machine translation models that use attention mechanisms are also discussed. The document concludes by noting areas for further development in reducing human effort in translation and increasing quality and effectiveness of speech-to-text and translation tools.
STREAMING PUNCTUATION: A NOVEL PUNCTUATION TECHNIQUE LEVERAGING BIDIRECTIONAL...
The document proposes a streaming punctuation technique that leverages bidirectional context for continuous speech recognition. It introduces a novel approach of streaming punctuation that discards decoder segmentation and shifts punctuation decision making to a powerful Transformer model. Experimental results show that streaming punctuation improves segmentation accuracy by 13.9% and achieves an average BLEU score gain of 0.66 for downstream machine translation tasks.
Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional...
While speech recognition Word Error Rate (WER) has reached human parity for English, continuous speech recognition scenarios such as voice typing and meeting transcriptions still suffer from segmentation and punctuation problems, resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions. Context within the segments produced by ASR decoders can be helpful but limiting in overall punctuation performance for a continuous speech session. In this paper, we propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEUscore improvement of 0.66 for the downstream task of Machine Translation (MT).
third_seminar
The document discusses developing a text-to-speech (TTS) system that can synthesize speech in a storytelling style from written story text. It aims to 1) develop a story TTS using a neutral TTS system and incorporating story-specific information, 2) develop a story TTS using a story speech corpus, and 3) model prosody patterns including pauses for story-style speech. The work involves building modules for story-specific emotion detection, prosody generation and incorporation. Models are developed to predict pause position and duration using rule-based, supervised and unsupervised methods. A multi-model framework improves pause duration prediction over a single model.
Ch09 combinatorialpatternmatching
This document discusses genomic repeats and techniques for finding repeats and matching patterns in DNA sequences. It covers using hash tables to store and retrieve DNA sequences, building keyword trees to represent sets of sequences, and constructing suffix trees to efficiently find all occurrences of a pattern in a string. Suffix trees allow pattern matching to be performed in linear time compared to quadratic time for a naive approach. The document also introduces the problem of approximate pattern matching and heuristic search algorithms used to quickly find similar matches in large genomes.
Reference Scope Identification in Citing Sentences
This document describes an approach to identify the scope of references in sentences containing multiple citations. The approach uses a conditional random field (CRF) sequence model with features including distance, position, part-of-speech tags, and syntactic dependencies. It first segments sentences using punctuation and conjunctions, then applies the CRF and a majority voting scheme to label each word as inside or outside the scope. The method achieved over 90% accuracy on a dataset of citations from ACL papers.
Vladyslav Hamolia "How to choose ASR (automatic speech recognition) system"
This document provides an overview of automatic speech recognition (ASR) technologies. It discusses the history and challenges of ASR, the general processing flow including speech representation techniques like MFCCs and acoustic and language modeling using HMMs and DNNs. It also introduces some open source ASR tools like CMU Sphinx and Kaldi and provides some ASR demonstration results on the TIMIT dataset using a BiLSTM model. The document concludes with questions for discussion.
ICCIT_presentation
The document presents a methodology for isolated Bangla word recognition and speaker detection using a Semantic Modular Time Delay Neural Network (MTDNN). The authors collected a dataset of 690 audio samples from 5 speakers uttering 46 Bangla words. They used the MTDNN framework to classify words and identify speakers with an accuracy of 82.66%. This represents an improvement over previous work on Bangla speech recognition. The authors conclude the MTDNN approach is effective for Bangla word recognition and speaker detection but recommend expanding the dataset and vocabulary in future work.
Sequence to sequence model speech recognition
The document discusses sequence to sequence models for speech recognition. It describes how traditional automatic speech recognition (ASR) works using acoustic, pronunciation, and language models. The document then introduces sequence to sequence models like Listen, Attend and Spell (LAS) which uses an encoder, attender, and decoder. LAS improves upon traditional ASR by integrating all models into a single neural network with attention and other optimizations like minimum word error rate training and scheduled sampling. Sequence to sequence models provide around 11% relative improvement in word error rate over traditional ASR systems.
nakai22apsipa_presentation.pdf
This document describes a multi-task adversarial training algorithm for improving the performance of multi-speaker neural text-to-speech (TTS) models, especially for voice cloning of unseen speakers. The proposed method augments GANSpeech training with an autoencoder interpolation regularization technique to diversify speaker variations during training. Evaluation shows the method generates higher quality synthesized speech for seen speakers compared to baselines, and also improves voice cloning for unseen speakers by reducing quality degradation. However, there is still a gap in speaker similarity between seen and unseen speakers, indicating room for further improvement.
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
This document summarizes a tutorial for developing a state-of-the-art named entity recognition framework using deep learning. The tutorial uses a bi-directional LSTM-CNN architecture with a CRF layer, as presented in a 2016 paper. It replicates the paper's results on the CoNLL 2003 dataset for NER, achieving an F1 score of 91.21. The tutorial covers data preparation from the dataset, word embeddings using GloVe vectors, a CNN encoder for character-level representations, a bi-LSTM for word-level encoding, and a CRF layer for output decoding and sequence tagging. The experience of presenting this tutorial to friends highlighted the need for detailed comments and explanations of each step and PyTorch functions.
Introducing data analysis: reads to results
Some reads could align to multiple locations:
Reference: AGTCTTAGGGACTTTATAC
AGTC TAGG
TTAC CTTT
GGGA
This is ambiguous - TTAC and GGGA could align in two places each.
We need more information (longer reads or paired reads) to resolve.
NLP Asignment Final Presentation [IIT-Bombay]
The final presentation I did with Lekha & Deepali for the Natural Language Processing assignments at IIT-Bombay.
Assignments included:
1: Spelling Correction
2: Part-of-speech Tagging
3: Metaphor Detection
BITS - Comparative genomics: the Contra tool
This is the last presentation of the BITS training on 'Comparative genomics'.
It reviews tthe Contra tool for detecting common transcription factor binding sites in sequences.
Thanks to Stefan Broos of the DMBR department of VIB
Multimodal Features for Search and Hyperlinking of Video Content
In the talk, I will discuss content-based retrieval in audio-visual collections. I will focus on retrieval of relevant segments of video using a textual query. In addition, I will describe techniques for detecting hyperlinks within audio-visual collections. Our retrieval system ranked first in the MediaEval 2014 Search and Hyperlinking shared task. The experiments were performed on almost 4000 hours of BBC broadcast video.
As the segmentation of the recordings shows to be crucial for high-quality video retrieval and hyperlinking, I will focus on segmentation strategies. I will show the possibility of employment of the prosodic and visual information into the segmentation process. Our decision tree-based segmentation proved to outperform fixed-length segmentation which regularly achieves the best results in the retrieval process. Visual and prosodic similarity are also explored in addition to the hyperlinking based on the subtitles and automatic transcripts. The employment of the visual similarity achieves a constant improvement, while the employment of the prosodic similarity shows a small but promising improvement too.
lec26_audio.pptx
This document discusses deep learning applications for audio, including speech recognition and text-to-speech. It outlines several deep learning models for automatic speech recognition, including DNN-HMM models that use deep neural networks for frame classification in HMM-based speech recognition systems. Recurrent neural networks like LSTMs are also mentioned for speech recognition. The document also briefly discusses text-to-speech models like WaveNet and Tacotron that generate speech from text using neural networks.
Filling the gaps
The slides present a text recovery method based on a probabilistic post-recognition processing of the output of an Optical Character Recognition system. The proposed method is trying to fill in the gaps of missing text resulted from the recognition process of degraded documents. For this task, a corpus of up to 5-grams provided by Google is used. Several heuristics for using this corpus for the fulfilment of this task are described after presenting the general problem and alternative solutions. These heuristics have been validated using a set of experiments that are also discussed together with the results that have been obtained.
Similar to CUNI at MediaEval 2013 Similar Segments in Social Speech Task (20)
Experiments with Segmentation Strategies for Passage Retrieval in Audio-Visua...
Experiments with Segmentation Strategies for Passage Retrieval in Audio-Visua...
CUNI at MediaEval 2012: Search and Hyperlinking Task
CUNI at MediaEval 2012: Search and Hyperlinking Task
Integration of speech recognition with computer assisted translation
Integration of speech recognition with computer assisted translation
STREAMING PUNCTUATION: A NOVEL PUNCTUATION TECHNIQUE LEVERAGING BIDIRECTIONAL...
STREAMING PUNCTUATION: A NOVEL PUNCTUATION TECHNIQUE LEVERAGING BIDIRECTIONAL...
Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional...
Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional...
Reference Scope Identification in Citing Sentences
Reference Scope Identification in Citing Sentences
Vladyslav Hamolia "How to choose ASR (automatic speech recognition) system"
Vladyslav Hamolia "How to choose ASR (automatic speech recognition) system"
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
Multimodal Features for Search and Hyperlinking of Video Content
Multimodal Features for Search and Hyperlinking of Video Content
More from Petra Galuscakova
Combining Evidence for Cross-language Information Retrieval
System combination has been extensively studied in monolingual information retrieval, but the problem is understudied in cross-language retrieval in which queries are expressed in one language, but documents are written in another. One notable characteristic of cross-language retrieval, however, is the potential for a greater diversity of system design, since translation and retrieval components both exhibit substantial design spaces. Due to the large diversity of the systems in cross-language retrieval, the potential range of combinations is orders of magnitude larger than in monolingual applications.
I show that evidence combination works well in cross-language retrieval, achieving improvements of 40% relative to the best single system. The best results are obtained using post-retrieval evidence combination, which is able to incorporate many diverse high-quality systems. Because hundreds of different systems can be built, the effectiveness of alternative approaches for managing the complexity is also explored. Both system clustering and expert judgment regarding diversity can help to limit the combinatorial growth of time complexity arising when selections among large numbers of systems need to be made.
Multimodal Features for Linking Television Content
How can acoustic and visual features be combined with text-based search methods applied on automatic transcripts and subtitles and help to retrieve television content.
Czech Malach Cross-lingual Speech Retrieval Test Collection
The document summarizes the Czech Malach Cross-lingual Speech Retrieval Test Collection, which contains 353 audio recordings selected from interviews in the USC Shoah Foundation's Visual History Archive. The collection includes automatic transcripts of the interviews in multiple formats, as well as manual topic annotations of segments and metadata. It is intended to help researchers in fields like information retrieval, machine translation, and social studies by providing a test bed for cross-lingual speech retrieval systems.
Audio Information for Hyperlinking of TV Content
This document summarizes research on hyperlinking TV content using audio information. The researchers retrieved segments similar to a query segment from television programs using subtitles, transcripts, and metadata. They addressed problems with automatic speech recognition transcripts like restricted vocabulary and lack of reliability by expanding queries and combining transcripts. Acoustic fingerprinting and similarity were also explored but did not improve results due to lack of content in the transcripts. Overall, query and data expansion along with combining transcripts was most effective for hyperlinking TV content using audio information.
Evaluácia tematického vyhľadávania v audiovizuálnych nahrávkach
4. výročie Centra vizuálnej histórie Malach
Application of Topic Segmentation in Audiovisual Information Retrieval
This document discusses various approaches to topic segmentation in audiovisual documents. It describes lexical cohesion-based methods like TextTiling and C99, as well as feature-based methods that use lexical, syntactic, prosodic, and visual features. It also covers segmentation of audio data using prosodic cues and segmentation of video data using visual similarity metrics. Finally, it discusses fusion approaches that combine information from multiple modalities for topic segmentation.
Penalty Functions for Evaluation Measures of Unsegmented Speech Retrieval
The document discusses evaluation methods for unsegmented speech retrieval and proposes modifications to the mean generalized average precision (mGAP) measure. It summarizes research that studied user behavior in a simulated retrieval task to navigate audio recordings and identify relevant passages. The study found that users prefer playback points before true starting points and are tolerant of points up to 1-2 minutes away. Based on these results, the document proposes modifications to the penalty function used in mGAP to give higher reward for points before passages and maintain reward within 1-2 minutes of true starting points. A comparison showed the modified function correlates highly with scores from the original measure.
More from Petra Galuscakova (7)
Combining Evidence for Cross-language Information Retrieval
Combining Evidence for Cross-language Information Retrieval
Multimodal Features for Linking Television Content
Multimodal Features for Linking Television Content
Czech Malach Cross-lingual Speech Retrieval Test Collection
Czech Malach Cross-lingual Speech Retrieval Test Collection
Evaluácia tematického vyhľadávania v audiovizuálnych nahrávkach
Evaluácia tematického vyhľadávania v audiovizuálnych nahrávkach
Application of Topic Segmentation in Audiovisual Information Retrieval
Application of Topic Segmentation in Audiovisual Information Retrieval
Penalty Functions for Evaluation Measures of Unsegmented Speech Retrieval
Penalty Functions for Evaluation Measures of Unsegmented Speech Retrieval
Recently uploaded
一比一原版(CU毕业证)卡尔顿大学毕业证如何办理
原件一模一样【微信：95270640】【卡尔顿大学毕业证CU学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理卡尔顿大学毕业证CU学位证本科学位证成绩单【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
原版办【微信号:BYZS866】【英属哥伦比亚大学毕业证(UBC毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
一比一原版(UO毕业证)渥太华大学毕业证如何办理
原件一模一样【微信：95270640】【渥太华大学毕业证UO学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证UO学位证毕业证offer【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
原版办理【微信号:BYZS866】【南十字星大学毕业证(SCU毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版制作【微信:41543339】【弗林德斯大学毕业证(Flinders毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同进口机器一比一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Recently uploaded (20)
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
CUNI at MediaEval 2013 Similar Segments in Social Speech Task
- 1. CUNI at MediaEval 2013 Similar Segments in Social Speech Task Petra Galuščáková and Pavel Pecina galuscakova@ufal.mff.cuni.cz Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University in Prague MediaEval, 18. 10. 2013
- 2. 2 Our approach ● The queries are created from the human transcripts of the query segments ● All words (from human transcripts) lying within the boundaries of the query segment in both tracks ● The recordings are segmented into overlapping passages ● Passages are indexed using the Terrier IR Platform ● Predefined settings, stopwords removal, Porter stemmer, pruning retrieved overlapping segments
- 3. 3 Segmentation ● Regular ● Segments of 50 seconds, 25 seconds long shift ● Machine Learning ● Decision about segment boundaries (for each word in the transcript) ● Classification trees ● Two types: segment boundaries identification and segment beginning identification ● Model trained and tuned on the human transcripts
- 4. 4 ML Segmentation ● Identify segment boundaries ● Each word in the transcripts belongs to a single segment ● Detect, whether the word is followed by a segment boundary ● The segment begins where the previous one ends → Segments do not overlap ● We need high precision of the decision ● Detect beginnings of segments ● 50 seconds long ● Segments can overlap ● We need high recall of the decision
- 5. 5 Features ● Cue words and cue tags (unigrams, bigrams and trigrams) ● Appear frequently at the segment boundary or are informative for the segment boundary ● Defined for segment beginning and for segment end ● e.g. for beginning: I, actually, exactly, … and for end: right?, there, so , … ● Letter cases ● Length of the silence before the word ● Division given in transcripts ● The output of the TextTiling algorithm ● Employ lexical cohesion
- 6. 6 Results Segmentation Beginnings Segmentation Ends Normal. SUR Normal. Recall F-measure REG REG 0.57 0.78 0.58 ML REG 0.65 0.90 0.67 ML ML 0.59 0.80 0.61 Segmentation Beginnings Segmentation Ends Normal. SUR Normal. Recall F-measure REG REG 0.87 1.19 0.90 ML REG 0.70 1.00 0.72 ML ML 0.65 0.90 0.67 Tab1. Human Transcripts Tab2. ASR Transcripts
- 7. 7 Conclusion
- 8. 8 Conclusion ● Overall best result is achieved using regular segmentation on the ASR transcripts ● Probably caused by approximated word times in human transcripts ● On the human transcripts, the ML-based segmentation outperforms the regular segmentation. ● On the ASR transcripts, the regular segmentation wins. ● ML-based segmentation searching for segment beginnings outperforms ML-segmentation searching for entire segments
- 9. 9 Thank you This research is supported by the Charles University Grant Agency (GA UK n. 920913) and the Czech Science Foundation (grant n. P103/12/G084).