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We investigate the detection of spoken terms in conversational speech using phoneme recognition with the objective of achieving smaller index size as well as faster search speed. Speech is processed and indexed as a sequence of one best phoneme sequence. W ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
This paper presents our approach for automatic speech recognition (ASR) of overlapping speech. Our system consists of two principal components: a speech separation component and a feature estmation component. In the speech separation phase, we first estima ...
The goal of this work is to provide robust and accurate speech detection for automatic speech recognition (ASR) in meeting room settings. The solution is based on computing long-term modulation spectrum, and examining specific frequency range for dominant ...
We investigate the detection of spoken terms in conversational speech using phoneme recognition with the objective of achieving smaller index size as well as faster search speed. Speech is processed and indexed as a sequence of one best phoneme sequence. W ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. Thisidentifica ...
There is magic (or is it witchcraft?) in a speech recognizer that transcribes continuous radio speech into text with a word accuracy of even not more than 50%. The extreme difficulty of this task, tough, is usually not perceived by the general public. This ...
We address the problem of keyword spotting in continuous speech streams when training and testing conditions can be different. We propose a keyword spotting algorithm based on sparse representation of speech signals in a time-frequency feature space. The t ...
This is the first book dedicated to uniting research related to speech and speaker recognition based on the recent advances in large margin and kernel methods. The first part of the book presents theoretical and practical foundations of large margin and ke ...
