Matsushita Electric Industrial Co., Ltd.

Technology Research Association of Medical Welfare Apparatus

Michael Galler

Jean-Claude Junqua

Hector Raul Javkin

Nancy Niedzielski

Luca Rigazio

Roland Kuhn

Matteo Contolini

Robert C Boman

Yi Zhao

Murat Karaorman

Ken Field

A computer-implemented method and apparatus is provided for processing a spoken request from a user. A speech recognizer converts the spoken request into a digital format. A frame data structure associates semantic components of the digitized spoken request with predetermined slots. The slots are indicative of data which are used to achieve a predetermined goal. A speech understanding module which is connected to the speech recognizer and to the frame data structure determines semantic components of the spoken request. The slots are populated based upon the determined semantic components. A dialog manager which is connected to the speech understanding module may determine at least one slot which is unpopulated based upon the determined semantic components and in a preferred embodiment may provide confirmation of the populated slots. A computer generated-request is formulated in order for the user to provide data related to the unpopulated slot. The method and apparatus are well-suited (but not limited) to use in a hand-held speech translation device.

Method for goal-oriented speech translation in hand-held devices using meaning extraction and dialogue

The speech recognizer incorporates a language model that reduces the number of acoustic pattern matching sequences that must be performed by the recognizer. The language model is based on knowledge of a pre-defined set of syntactically defined content and includes a data structure that organizes the content according to acoustic confusability. A spelled name recognition system based on the recognizer employs a language model based on classes of letters that the recognizer frequently confuses for one another. The language model data structure is optionally an N-gram data structure, a tree data structure, or an incrementally configured network that is built during a training sequence. The incrementally configured network has nodes that are selected based on acoustic distance from a predetermined lexicon.

Method and apparatus using probabilistic language model based on confusable sets for speech recognition

The input speech is segmented using plural grammar networks, including a network that includes a filler model designed to represent noise or extraneous speech. Recognition processing results in plural lists of candidates, each list containing the N-best candidates generated. The lists are then separately aligned with the dictionary of valid names to generate two lists of valid names. The final recognition pass combines these two lists of names into a dynamic grammar and this dynamic grammar may be used to find the best candidate name using Viterbi recognition. A telephone call routing application based on the recognition system selects the best candidate name corresponding to the name spelled by the user, whether the user pronounces the name prior to spelling, or not.

Speech recognition system employing multiple grammar networks

The present invention eliminates injection noise in speech produced by esophageal speakers. A speech input signal is digitized. One copy of the digitized signal is used for analysis and the other is passed through a gain switch to an amplifier as output. A Fast Fourier Transform and a mean value of the digitized speech input signal is calculated. The Fast Fourier Transform (FFT) is passed through a morphological filter to produce a filtered spectrum. An occurrence of injection noise is detected by calculating a derivative of the filtered spectrum and determining from the mean value and the derivative a location and value of a largest peak and a second largest peak in the filtered spectrum. If the largest peak is lower in frequency than the second largest peak, and if all points above 2 KHz are less than the mean, then an occurrence of injection noise has been detected. An occurrence of silence is detected by center-clipping the filtered spectrum and determining whether there is any energy within a sliding 10 millisecond window for a predetermined amount of time. If no energy is detected within a sliding 10 millisecond window for a predetermined amount time, then an occurrence of silence has been detected. The output speech signal is passed after the occurrence of injection noise has been detected; and is blocked following an occurrence of silence.

Esophageal speech injection noise detection and rejection

Injection noise and silence are detected in an input speech signal and an external amplifier is switched on or off based on the detected injection noise or silence. The input speech signal is digitized and a first copy of the digitized signal is preemphasized. After the input speech signal is preemphasized, a predetermined number of Mel-frequency cepstral coefficients (MFCCs) and difference cepstra are calculated for each window of the speech signal. A measure of signal energy and a measure of the rate of change of the signal energy is computed. A second copy of the digitized input speech signal is processed using amplitude summation or by differencing a center-clipped signal. The measures of signal energy, rate of change of the signal energy, the Mel coefficients, difference cepstra, and either the amplitude summation value or the differenced value are combined to form an observation vector. Hidden Markov Model (HMM) based decoding is used on the observation vector to detect the occurrence of injection noise or silence. A gain switch on an external speech amplifier is turned on after an occurrence of injection noise and remains on for the duration of speech and the amplifier is turned off when an occurrence of silence is detected.

Enhancement of esophageal speech by injection noise rejection

The call routing device plugs into existing extensions of the office telephone network or PBX system and acts as a 'virtual' operator, prompting incoming callers to spell the name of the desired recipient. The speech recognizer uses a multipass procedure employing Hidden Markov Models and dynamic programming. The N-best hypotheses are propagated between passes, allowing the more computationally costly routines to be reserved until the final pass, when the size of the search space is significantly reduced. The routing device prompts the user to confirm that the selected name is correct, whereupon the device signals the telephone network to automatically switch the incoming call to the telephone extension of the selected recipient.

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Santa Barbara, CA, United States of America

Michael Galler