Conference paper
Single-Channel Speech Separation using Sparse Non-Negative Matrix Factorization
We apply machine learning techniques to the problem of separating multiple speech sources from a single microphone recording. The method of choice is a sparse non-negative matrix factorization algorithm, which in an unsupervised manner can learn sparse representations of the data. This is applied to the learning of personalized dictionaries from a speech corpus, which in turn are used to separate the audio stream into its components.
We show that computational savings can be achieved by segmenting the training data on a phoneme level. To split the data, a conventional speech recognizer is used. The performance of the unsupervised and supervised adaptation schemes result in significant improvements in terms of the target-to-masker ratio.
Language: | English |
---|---|
Year: | 2007 |
Proceedings: | Spoken Language Proceesing, ISCA International Conference on (INTERSPEECH) |
Types: | Conference paper |
ORCIDs: | Schmidt, Mikkel N. |