Unsupervised learning of timefrequency patches as a noise-robust representation of speech
We present a self-learning algorithm using a bottom-up based approach to automatically discover, acquire and recognize the words of a language. First, an unsupervised technique using non-negative matrix factorization (NMF) discovers phone-sized timefrequency patches into which speech can be decomposed. The input matrix for the NMF is constructed for static and dynamic speech features using a spectral representation of both short and long acoustic events. By describing speech in terms of the discovered timefrequency patches, patch activations are obtained which express to what extent each patch is present across time. We then show that speaker-independent patterns appear to recur in these patch activations and how they can be discovered by applying a second NMF-based algorithm on the co-occurrence counts of activation events. By providing information about the word identity to the learning algorithm, the retrieved patterns can be associated with meaningful objects of the language. In case of a small vocabulary task, the system is able to learn patterns corresponding to words and subsequently detects the presence of these words in speech utterances. Without the prior requirement of expert knowledge about the speech as is the case in conventional automatic speech recognition, we illustrate that the learning algorithm achieves a promising accuracy and noise robustness.
matrix factorization automatic speech recognition noise robustness acoustic signal analysis language acquisition