Abstract: A method and associated system for recognizing speech using multiple speech recognition algorithms. The method includes receiving speech at a microphone installed in a vehicle, and determining results for the speech using a first algorithm, e.g., embedded locally at the vehicle. Speech results may also be received at the vehicle for the speech determined using a second algorithm, e.g., as determined by a remote facility. The results for both may include a determined speech topic and a determined speech slotted value, along with corresponding confidence levels for each. The method may further include using at least one of the determined first speech topic and the received second speech topic to determine the topic associated with the received speech, even when the first speech topic confidence level of the first speech topic, and the second speech topic confidence level of the second speech topic are both a low confidence level.

Abstract: A method and associated system for recognizing speech using multiple speech recognition algorithms. The method includes receiving speech at a microphone installed in a vehicle, and determining results for the speech using a first algorithm, e.g., embedded locally at the vehicle. Speech results may also be received at the vehicle for the speech determined using a second algorithm, e.g., as determined by a remote facility. The results for both may include a determined speech topic and a determined speech slotted value, along with corresponding confidence levels for each. The method may further include using at least one of the determined first speech topic and the received second speech topic to determine the topic associated with the received speech, even when the first speech topic confidence level of the first speech topic, and the second speech topic confidence level of the second speech topic are both a low confidence level.

Abstract: A system and method of regulating speech output by a text-to-speech (TTS) system includes: evaluating speech that has been converted from text using an initial speech quality test before presentation to a user; applying a classification test to the evaluated speech if the evaluated speech falls below a threshold based on the initial speech quality test; generating an abnormal speech classification for the evaluated speech; and applying a corrective action to the evaluated speech based on the abnormal speech classification.

Abstract: A system and method of regulating speech output by a text-to-speech (TTS) system includes: evaluating speech that has been converted from text using an initial speech quality test before presentation to a user; applying a classification test to the evaluated speech if the evaluated speech falls below a threshold based on the initial speech quality test; generating an abnormal speech classification for the evaluated speech; and applying a corrective action to the evaluated speech based on the abnormal speech classification.

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 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 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 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 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 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.