Motorola Corporation

Orhan Karaali

Noel S Massey

Gerald Edward Corrigan

Corey Andrew Miller

Andrew William Mackie

Ira A Gerson

Dale William Russell

Harry M Bliss

Richard John Peterson

A method (,), device (,), and system (,) provide part-of-speech disambiguation for words based on hybrid neural-network and stochastic processing. The method disambiguates the part-of-speech tags of text tokens by obtaining a set of probabilistically annotated tags for each text token, determining a locally predicted tag for each text token based on the local context of the text token, determining an alternative tag for each text token based on the expanded context of the text token, and choosing between the locally predicted tag and the alternative tag when the locally predicted tag and the alternative tag are different.

Method, device and system for part-of-speech disambiguation

A method (,), device (,) and system (,) provide, in response to text/linguistic input, one of a set of pre-determined meanings which is the most likely intended meaning of that input. A trained meaning discriminator is generated from an annotated training corpus and a meaning discriminator trainer. The trained meaning discriminator generates a meaning vector from an input utterance. The intended meaning encoder analyzes the meaning vector to determine the most likely intended meaning and confidence measures.

Method, device and system for noise-tolerant language understanding

A method (2000), device (2200) and article of manufacture (2300) provide, in response to lexical pronunciation information, efficient generation of postlexical pronunciation information. A method is presented for providing, in response to a lexical pronunciation, efficient generation of a postlexical pronunciation, including the steps of: determining lexical phones, lexical features, and boundary information for a predetermined portion of text; and utilizing a pretrained neural network that was pretrained using lexical phones, postlexical phones, lexical features, and boundary information to generate a neural network hypothesis for a postlexical pronunciation of the predetermined portion of text.

Method device and article of manufacture for neural-network based

The present invention teaches a method (400), device and system (300) utilizing at least one of: mapping a sequence of phones to a sequence of articulatory features and utilizing prominence and boundary information, in addition to a predetermined set of rules for type, phonetic context, syntactic and prosodic context for phones to provide provide a system that generates segment durations efficiently with a small training set.

Method, device and system for generating segment durations in a

A method (2000), device (2200) and article of manufacture (2300) provide, in response to orthographic information, efficient generation of a phonetic representation. The method provides for, in response to orthographic information, efficient generation of a phonetic representation, using the steps of: inputting an orthography of a word and a predetermined set of input letter features; utilizing a neural network that has been trained using automatic letter phone alignment and predetermined letter features to provide a neural network hypothesis of a word pronunciation.

Method, device and article of manufacture for neural-network based

A method (400, 900, 1000, 1100), device (1205, 1306) and system (1207, 1308) provide memory-efficient encoding of the random-access lexicon for a text-to-speech synthesis system. The method generates word pronunciations based on efficient retrieval of stored pronunciation information and disambiguation information for an input word, by dividing the input word into a stem and a maximal suffix having a maximal suffix code and then generating at least one word pronunciation based on the stem and the maximal suffix.

Method, device and system for a memory-efficient random-access

A method (400), device and system (300) provide, in response to linguistic information, efficient generation of a parametric representation of speech using a neural network. The method provides, in response to linguistic information efficient generation of a refined parametric representation of speech, comprising the steps of: A) using a data selection module to retrieve representative parameter vectors for each segment description according to the phonetic segment type and the phonetic segment types included in adjacent segment descriptions; B) interpolating between the representative parameter vectors according to the segment descriptions and duration to provide interpolated statistical parameters; C) converting the interpolated statistical parameters and linguistic information to neural network input parameters; D) utilizing a statistically enhanced neural network/neural network with post-processor to provide neural network output parameters that correspond to a parametric representation of speech; and converting the neural network output parameters to a refined parametric representation of speech.

Method, device and system for using statistical information to reduce

Text may be converted to audible signals, such as speech, by first training a neural network 106 using recorded audio messages 204. To begin the training, the recorded audio messages are converted into a series of audio frames 205 having a fixed duration 213. Then, each audio frame is assigned a phonetic representation 203 and a target acoustic representation 208, where the phonetic representation 203 is a binary word that represents the phone and articulation characteristics of the audio frame, while the target acoustic representation 208 is a vector of audio information such as pitch and energy. After training, the neural network 106 is used in conversion of text into speech. First, text that is to be convened is translated to a series of phonetic frames 401 of the same form as the phonetic representations 208 and having the fixed duration 213. Then the neural network produces acoustic representations in response to context descriptions 207 that include some of the phonetic frames 401. The acoustic representations are then converted into a speech wave form by a synthesizer 107.

Method and apparatus for converting text into audible signals using a

A receiver (100) capable of synchronizing to a received signal (302) samples the received signals (302) at a plurality of sample points, and stores all occurrences of zero-crossings corresponding to the plurality of sample points. A processor (212) synchronizes the receiver (100) to the received signal (302) in response to the number of zero-crossings.

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Rolling Meadows, IL, United States of America

Orhan Karaali