Abstract: An approach automated processing for audio or other data series or signals, which is applicable where little or no transcribed training data is available, makes uses identification of self-organizing units (SOUs) in conjunction with automated creation of, or augmentation of an existing dictionary, with “pseudo-words” or tokens represented in terms of the SOUs. In some examples, the dictionary is iteratively updated (e.g., augmented) during training, optionally with updating of models of the SOUs during the iteration.

Abstract: A system is provided that trains a speech processing device to recognize any spoken language. The speech processing device is trained by receiving a group of speech frames (302). The group of speech frames is grouped into a group of segments (304). A model of each of the segments is created (306) and the segments are compared (308). The segments are grouped into clusters based on comparisons of the segments (310). A model for each of the clusters is built (312). The built cluster models enable the labeling of segments from speech in the language. Labeled segments (i.e., recognizer output) may be used to train topic classifiers. Speech topics may be classified on the basis of the generated labeled segments.

Abstract: An improved topic discriminator includes an integrated speech recognizer or word and phrase spotter as part of a speech event detector, and a topic classifier trained on topic-dependent event frequencies. The event frequencies are determined from either or both transcribed data with a text event detector and untranscribed speech data with a speech event detector. In accordance with another aspect of the present invention, the phrase spotter is used to detect the presence of phrases without the need of parsing the output of a speech recognizer's hypothesized transcription. In accordance with another aspect of the invention, an improved technique is used to select a subset of the potential speech events on whose event frequencies the topic discrimination decision is made. Finally, in accordance with another aspect of the invention, an improved method of topic modeling is used to improve the performance of the topic discriminator.

Abstract: A code signal representing a sequence of code symbols carrying digital information is generated with the frequency components of the code signal being essentially confined to a preselected signalling band lying within the bandwidth of an audio signal within which the code signal is to be embedded. The audio signal is continuously frequency analyzed over a frequency band encompassing the signalling band and the code signal is dynamically filtered as a function of the analysis thereby to provide a modified code signal with frequency components which are, at each time instant, essentially a preselected small proportion of the levels of the corresponding audio signal frequency components. Accordingly, the modified code signal can be combined with the audio signal to obtain a composite audio signal which is not readily distinguishable from the original audio signal by listening.