Abstract: Embodiments are associated with determination of a first plurality of multi-dimensional vectors, each of the first plurality of multi-dimensional vectors representing speech of a target speaker, determination of a multi-dimensional vector representing a speech signal of two or more speakers, determination of a weighted vector representing speech of the target speaker based on the first plurality of multi-dimensional vectors and on similarities between the multi-dimensional vector and each of the first plurality of multi-dimensional vectors, and extraction of speech of the target speaker from the speech signal based on the weighted vector and the speech signal.

Abstract: The subject disclosure is directed towards training a classifier for spoken utterances without relying on human-assistance. The spoken utterances may be related to a voice menu program for which a speech comprehension component interprets the spoken utterances into voice menu options. The speech comprehension component provides confirmations to some of the spoken utterances in order to accurately assign a semantic label. For each spoken utterance with a denied confirmation, the speech comprehension component automatically generates a pseudo-semantic label that is consistent with the denied confirmation and selected from a set of potential semantic labels and updates a classification model associated with the classifier using the pseudo-semantic label.

Abstract: The subject disclosure is directed towards training a classifier for spoken utterances without relying on human-assistance. The spoken utterances may be related to a voice menu program for which a speech comprehension component interprets the spoken utterances into voice menu options. The speech comprehension component provides confirmations to some of the spoken utterances in order to accurately assign a semantic label. For each spoken utterance with a denied confirmation, the speech comprehension component automatically generates a pseudo-semantic label that is consistent with the denied confirmation and selected from a set of potential semantic labels and updates a classification model associated with the classifier using the pseudo-semantic label.

Abstract: A computer-implemented speech recognition system described herein includes a receiver component that receives a plurality of detected units of an audio signal, wherein the audio signal comprises a speech utterance of an individual. A selector component selects a subset of the plurality of detected units that correspond to a particular time-span. A generator component generates at least one feature with respect to the particular time-span, wherein the at least one feature is one of an existence feature, an expectation feature, or an edit distance feature. Additionally, a statistical speech recognition model outputs at least one word that corresponds to the particular time-span based at least in part upon the at least one feature generated by the feature generator component.

Abstract: Described is the use of utterance classification based methods and other machine learning techniques to provide a telephony application or other voice menu application (e.g., an automotive application) that need not use Context-Free-Grammars to determine a user's spoken intent. A classifier receives text from an information retrieval-based speech recognizer and outputs a semantic label corresponding to the likely intent of a user's speech. The semantic label is then output, such as for use by a voice menu program in branching between menus. Also described is training, including training the language model from acoustic data without transcriptions, and training the classifier from speech-recognized acoustic data having associated semantic labels.

Abstract: Described is a technology in which information retrieval (IR) techniques are used in a speech recognition (ASR) system. Acoustic units (e.g., phones, syllables, multi-phone units, words and/or phrases) are decoded, and features found from those acoustic units. The features are then used with IR techniques (e.g., TF-IDF based retrieval) to obtain a target output (a word or words).

Abstract: A computer-implemented speech recognition system described herein includes a receiver component that receives a plurality of detected units of an audio signal, wherein the audio signal comprises a speech utterance of an individual. A selector component selects a subset of the plurality of detected units that correspond to a particular time-span. A generator component generates at least one feature with respect to the particular time-span, wherein the at least one feature is one of an existence feature, an expectation feature, or an edit distance feature. Additionally, a statistical speech recognition model outputs at least one word that corresponds to the particular time-span based at least in part upon the at least one feature generated by the feature generator component.