Nuance Communications Austria Gmbh

Koninklijke Philips Corporation N.v.

Nuance Communications, Inc.

Zsolt Saffer

A method and a device () for automatically generating vocabulary entry from input acoustic data (), comprising a vocabulary entry type-specific acoustic phonetic transcription module (; T) and a classifier module (') for the classification of vocabulary entry types on the basis of the phonetic structure, wherein the classification of vocabulary entries is carried out in accordance with a number of predetermined types; and vocabulary entry type-specific phoneme-to-grapheme conversion means (), to derive the respective vocabulary entries comprising a pair of a phonetic transcription and its grapheme form.

Method and device for generating vocabulary entry from acoustic data

The invention deals with speech recognition, such as a system for recognizing words in continuous speech. A speech recognition system is disclosed which is capable of recognizing a huge number of words, and in principle even an unlimited number of words. The speech recognition system comprises a word recognizer for deriving a best path through a word graph, and wherein words are assigned to the speech based on the best path. The word score being obtained from applying a phonemic language model to each word of the word graph. Moreover, the invention deals with an apparatus and a method for identifying words from a sound block and to computer readable code for implementing the method.

Speech recognition system with huge vocabulary

In a method (M) to detect a noise signal (PS, PS, PS) in a digital audio signal (EAS), it is provided that the audio signal (EAS) is divided into successive signal sections (SAS), and the energy contents of successive signal sections (SAS) are determined, and the energy contents of a signal section (SAS) are evaluated in relation to an energy threshold (ET), and that the occurrence of at least one high-energy signal section having an energy content above the energy threshold (ET), and the occurrence of at least one signal section (SAS) preceding the at least one high-energy signal section and having an energy content below the energy threshold (ET), and the occurrence of at least one signal section (SAS) following the at least one high-energy signal section and having an energy content below the energy threshold (ET) are detected, and that a quantity of signal sections (SAS) that precede the at least one high-energy signal section and a quantity of high-energy signal sections and a quantity of signal sections (SAS) that follow the high-energy signal section are counted.

Clicking noise detection in a digital audio signal

In a speech recognition device () for recognizing text information (TI) corresponding to speech information (SI), wherein speech information (SI) can be characterized in respect of language properties, there are firstly provided at least two language-property recognition means (), each of the language-property recognition means () being arranged, by using the speech information (SI), to recognize a language property assigned to said means and to generate property information (ASI, LI, SGI, CI) representing the language property that is recognized, and secondly there are provided speech recognition means () that, while continuously taking into account the at least two items of property information (ASI, LI, SGI, CI), are arranged to recognize the text information (TI) corresponding to the speech information (SI).

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Vienna, Austria

Zsolt Saffer