Abstract: A sampling-rate-independent method of automated speech recognition (ASR). Speech energies of a plurality of codebooks generated from training data created at an ASR sampling rate are compared to speech energies in a current frame of acoustic data generated from received audio created at an audio sampling rate below the ASR sampling rate. A codebook is selected from the plurality of codebooks, and has speech energies that correspond to speech energies in the current frame over a spectral range corresponding to the audio sampling rate. Speech energies above the spectral range are copied from the selected codebook and appended to the current frame.

Abstract: A method of and system for automated distortion classification. The method includes steps of (a) receiving audio including a user speech signal and at least some distortion associated with the signal; (b) pre-processing the received audio to generate acoustic feature vectors; (c) decoding the generated acoustic feature vectors to produce a plurality of hypotheses for the distortion; and (d) post-processing the plurality of hypotheses to identify at least one distortion hypothesis of the plurality of hypotheses as the received distortion. The system can include one or more distortion models including distortion-related acoustic features representative of various types of distortion and used by a decoder to compare the acoustic feature vectors with the distortion-related acoustic features to produce the plurality of hypotheses for the distortion.

Abstract: A method for recognizing speech involves reciting, into a speech recognition system, an utterance including a numeric sequence that contains a digit string including a plurality of tokens and detecting a co-articulation problem related to at least two potentially co-articulated tokens in the digit string. The numeric sequence may be identified using i) a dynamically generated possible numeric sequence that potentially corresponds with the numeric sequence, and/or ii) at least one supplemental acoustic model. Also disclosed herein is a system for accomplishing the same.

Abstract: A method of ambient noise injection for use with speech recognition in a production vehicle. The method includes the steps of monitoring audio including user speech, receiving an utterance from the user speech, retrieving vehicle-specific ambient noise, and prepending the vehicle-specific ambient noise to the utterance before pre-processing and decoding the utterance.

Abstract: A system and method for providing speech recognition functionality offers improved accuracy and robustness in noisy environments having multiple speakers. The described technique includes receiving speech energy and converting the received speech energy to a digitized form. The digitized speech energy is decomposed into features that are then projected into a feature space having multiple speaker subspaces. The projected features fall either into one of the multiple speaker subspaces or outside of all speaker subspaces. A speech recognition operation is performed on a selected one of the multiple speaker subspaces to resolve the utterance to a command or data.

Abstract: A speech recognition method comprising the steps of: storing multiple recognition models for a vocabulary set, each model distinguished from the other models in response to a Lombard characteristic, detecting at least one speaker utterance in a motor vehicle, selecting one of the multiple recognition models in response to a Lombard characteristic of the at least one speaker utterance, utilizing the selected recognition model to recognize the at least one speaker utterance; and providing a signal in response to the recognition.

Abstract: A speech recognition adaptation method for a vehicle having a telematics unit with an embedded speech recognition system. Speech is received and pre-processed to generate acoustic feature vectors, and an adaptation parameter is applied to the acoustic feature vectors to yield transformed acoustic feature vectors. The transformed acoustic feature vectors are decoded and a hypothesis of the speech is selected, and the adaptation parameter is trained using acoustic feature vectors from the hypothesis. The method also includes one or more of the following steps: the speech is observed for a certain characteristic and the trained adaptation parameter is saved in accordance with the certain characteristic for use in transforming feature vectors of subsequent speech having the certain characteristic; use of the trained adaptation parameter persists from one vehicle ignition cycle to the next; and use of the trained adaptation parameter is ceased upon detection of a system fault.

Abstract: A method for managing user nametags for a vehicle communications system includes receiving a nametag transfer request at a call center from a source, determining a target vehicle based on the request, generating a nametag data file including a text data file, a phoneme set, and an audio data file for each nametag, and transmitting the nametag data file to the target vehicle.

Abstract: A method of dialing phone numbers using an in-vehicle speech recognition system includes receiving speech input at a vehicle, separating the speech input into a word segment and a digit segment, identifying the letters in a word segment, converting the letters in the word segment to digits, and operating an alphanumeric keypad based on the digit speech segment and the converted word segment.

Abstract: A speech recognition method includes the steps of receiving speech in a vehicle, extracting acoustic data from the received speech, and applying a vehicle-specific inverse impulse response function to the extracted acoustic data to produce normalized acoustic data. The speech recognition method may also include one or more of the following steps: pre-processing the normalized acoustic data to extract acoustic feature vectors; decoding the normalized acoustic feature vectors using as input at least one of a plurality of global acoustic models built according to a plurality of Lombard levels of a Lombard speech corpus covering a plurality of vehicles; calculating the Lombard level of vehicle noise; and/or selecting the at least one of the plurality of global acoustic models that corresponds to the calculated Lombard level for application during the decoding step.

Abstract: A system and method for tuning a speech recognition engine to an individual microphone using a database containing acoustical models for a plurality of microphones. Microphone performance characteristics are obtained from a microphone at a speech recognition engine, the database is searched for an acoustical model that matches the characteristics, and the speech recognition engine is then modified based on the matching acoustical model.

Abstract: A method for managing user nametags for a vehicle communications system, the method including receiving a nametag transfer request at a call center from a source, determining a target vehicle associated with the source based on the request and modifying a source nametag data file based on an acoustic profile of the target vehicle, wherein the source nametag data file is one of an audio file and a text file.

Abstract: A method of circumstantial speech recognition in a vehicle. A plurality of parameters associated with a plurality of vehicle functions are monitored as an indication of current vehicle circumstances. At least one vehicle function is identified as a candidate for user-intended ASR control based on user interaction with the vehicle. The identified vehicle function is then used to disambiguate between potential commands contained in speech received from the user.

Abstract: A method of speech to DTMF generation involving ASR-enabled and DTMF-controlled communications systems. The ASR-enabled system is used to recognize speech received from the DTMF-controlled telecommunications system using sampling rate independent speech recognition. It then identifies a speech segment contained in the speech received from the DTMF-controlled system that corresponds with at least one keyword associated with user-defined data. Then, the ASR-enabled system transmits at least one DTMF signal to the DTMF-controlled system in response to the identified speech segment. This allows a user of an ASR-enabled system such as a vehicle telematics unit to at least partially automate access to the DTMF-controlled system using the telematics unit, so that voice mailbox numbers, passwords, and the like normally entered via a telephone keypad can be automatically sent to the DTMF-controlled system from the telematics unit without having to be manually input each time by the user.

Abstract: A method of ambient noise injection for use with speech recognition in a production vehicle. The method includes the steps of monitoring audio including user speech, receiving an utterance from the user speech, retrieving vehicle-specific ambient noise, and prepending the vehicle-specific ambient noise to the utterance before pre-processing and decoding the utterance.

Abstract: A sampling-rate-independent method of automated speech recognition (ASR). Speech energies of a plurality of codebooks generated from training data created at an ASR sampling rate are compared to speech energies in a current frame of acoustic data generated from received audio created at an audio sampling rate below the ASR sampling rate. A codebook is selected from the plurality of codebooks, and has speech energies that correspond to speech energies in the current frame over a spectral range corresponding to the audio sampling rate. Speech energies above the spectral range are copied from the selected codebook and appended to the current frame.

Abstract: A voice dialing method includes the steps of receiving an utterance from a user, decoding the utterance to identify a recognition result for the utterance, and communicating to the user the recognition result. If an indication is received from the user that the communicated recognition result is incorrect, then it is added to a rejection reference. Then, when the user repeats the misunderstood utterance, the rejection reference can be used to eliminate the incorrect recognition result as a potential subsequent recognition result. The method can be used for single or multiple digits or digit strings.

Abstract: A speech recognition method includes the steps of receiving input speech containing vocabulary, processing the input speech with a grammar to obtain N-best hypotheses and associated parameter values, and determining whether a first-best hypothesis of the N-best hypotheses is confusable with any vocabulary within the grammar. The first-best hypothesis is accepted as recognized speech corresponding to the received input speech if the first-best hypothesis is not determined to be confusable with any vocabulary within the grammar. Where the first-best hypothesis is determined to be confusable, at least one parameter value of the first-best hypothesis can be compared to at least one threshold value. The first-best hypothesis can be accepted as recognized speech corresponding to the received input speech, if the parameter value of the first-best hypothesis is greater than the threshold value.

Abstract: A speech recognition method includes the steps of receiving a location-specific command from a vehicle occupant, and adjusting either the shape or magnitude of a pick up pattern of at least one microphone in response to the location-specific command. The microphone adjustment can be carried out by electronically or physically steering the pick-up pattern.

Abstract: A speech recognition adaptation method for a vehicle having a telematics unit with an embedded speech recognition system. Speech is received and pre-processed to generate acoustic feature vectors, and an adaptation parameter is applied to the acoustic feature vectors to yield transformed acoustic feature vectors. The transformed acoustic feature vectors are decoded and a hypothesis of the speech is selected, and the adaptation parameter is trained using acoustic feature vectors from the hypothesis. The method also includes one or more of the following steps: the speech is observed for a certain characteristic and the trained adaptation parameter is saved in accordance with the certain characteristic for use in transforming feature vectors of subsequent speech having the certain characteristic; use of the trained adaptation parameter persists from one vehicle ignition cycle to the next; and use of the trained adaptation parameter is ceased upon detection of a system fault.