At least eight large vessels are being held up due to the incident.
Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the majority of languages in the world.
This thesis addresses this lack-of-annotation problem and presents two completely unsupervised spoken keyword spotting systems that do not require any transcribed data. In the first system, a Gaussian Mixture Model is trained to label speech frames with a Gaussian posteriorgram, without any transcription information.
Given several spoken samples of a keyword, a segmental dynamic time warping is used to compare the Gaussian posteriorgrams between keyword samples and test utterances. The keyword detection result is then obtained by ranking the distortion scores of all the test utterances.
In the second system, to avoid the need for spoken samples, a Joint-Multigram model is used to build a mapping from the keyword text samples to the Gaussian component indices.
A keyword instance in the test data can be detected by calculating the similarity score of the Gaussian component index sequences between keyword samples and test utterances.
The result demonstrates the viability and effectiveness of the two systems. Furthermore, encouraged by the success of using unsupervised methods to perform keyword spotting, we present some preliminary investigation on the unsupervised detection of acoustically meaningful units in speech.
Principle Research Scientist Acknowledgments I would like to thank my advisor, James Glass, for his encouragement, patience and every discussion that guided me through the research in this thesis.
In addition, I would like to thank T. Hazen for helpful discussions and insightful comment about this research. The research experiments in this thesis were made possible by assistantships provided by the entire Spoken Language Systems group.
Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. Finally, I would certainly like to thank my parents, who always give constant love and support to me.
Contents 1 Introduction 1. Unsupervised Keyword Spotting Systems. GMM Training and Clustering. Unfortunately, such valuable linguistic resources are unlikely to be available for the majority of languages in the world, especially for less frequently used languages.
For example, commercial ASR engines typically support or fewer languages . Despite substantial development efforts to create annotated linguistic resources that can be used to support ASR development , the results fall dramatically short of covering the nearly 7, human languages spoken around the globe .
For this reason, there is a need to explore ASR training methods which require significantly less language-specific data than conventional methods.
The problem of keyword spotting in audio data has been explored for many years, and researchers typically use ASR technology to detect instances of particular keywords in a speech corpus . Although large-vocabulary ASR methods have been shown to be very effective , a popular method incorporates parallel filler or background acoustic models to compete with keyword hypotheses [44, 27].
These keyword spotting methods typically require large amounts of transcribed data for training the acoustic model. For instance, the classic filler model requires hundreds of minutes of speech data transcribed at the word level , while in the phonetic lattice matching based approaches [39, 21], the training of a phonetic recognizer needs detailed transcription at the phone level.
The required annotation work is not only time con- suming, it also requires linguistic expertise for providing the necessary annotations which can be a barrier to new languages. In this thesis, we focus on investigating techniques to perform the keyword spotting task without any transcribed data.
The results demonstrate the feasibility and effectiveness of our unsupervised learning framework for the task of keyword spotting. A related question is what techniques can be performed well using unsupervised techniques in comparison to more conventional supervised training methods.
These two questions are the fundamental motivation of our research. Specifically, the idea of investigating unsupervised learning of speech-related tasks is motivated by the recent trends in data driven methods towards unsupervised largescale speech data processing.
As mentioned, the speed of the speech data production is much faster than data transcription can be performed. We need to find new ways of dealing with untranscribed data instead of waiting until enough transcription work is done.
Transcription work is not only time consuming, but also requires some linguistic knowledge. Finally, hiring linguistic professionals to perform these tasks can be very expensive. The idea of building an unsupervised keyword spotting system is motivated by the trend towards finding useful information from data in multi-media formats.Scribd es red social de lectura y publicación más importante del mundo.
Author College Application Essay Posted on March 30, March 30, Bramble Corporation sells rock-climbing products and also operates an indoor climbing facility for climbing enthusiasts. During the last part of , Bramble had the following transactions related to notes payable.
the first is like a 15 page essay and the 2nd is a textbook w/ both mathematical & non-mathematical treatments on the subject.
the journal of finance had not one reference of "fractal" "non-scalable" "scale invariant" etc. they had about ten million references for "volatility" though. Search the history of over billion web pages on the Internet. O Scribd é o maior site social de leitura e publicação do mundo.
Section 18 Communications and Information Technology This section presents statistics on the various communications media: tele-phone, telegraph, radio, television, news-.