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This paper presents a strategy for efficiently selecting informative data from large corpora of untranscribed speech. Confidence-based selection methods (i.e., selecting utterances we are least confident about) have been a popular approach, though they only look at the top hypothesis when selecting utterances and tend to select outliers, therefore, not always improving overall recognition accuracy...
The Query-by-Humming (QBH) system allows users to retrieve songs by singing/humming. In this paper we propose a phrase-level piecewise linear scaling algorithm for melody match. Musical phrase boundaries are predicted for the query to split it to phrases. The boundaries of melody fragment corresponding to each phrase are allowed for adjusting in a limited scope. The algorithm employs Dynamic Programming...
To achieve a good balance between matching accuracy and computation efficiency is a key challenge for query-by-humming (QBH) system. In this paper, we propose an approach of n-gram based fast match. Our n-gram method uses a robust statistical note transcription as well as error compensation method based on the analysis of frequent transcription errors. The effectiveness of our approach has been evaluated...
This paper presents a robust voice-melody transcription system using a speech recognition framework. While many previous voice-melody transcription systems have utilized non-statistical approaches, statistical recognition technology can potentially achieve more robust results. A cepstrum-based acoustic model is employed to avoid the hard-decisions that have to be made when using explicit voiced-unvoiced...
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