Abstract: Methods and systems for optimizing and audio communication system, including detecting a surrounding noise profile, determining one or more states of one or more noise-related conditions corresponding to the surrounding noise profile, and associating the one or more states of the one or more noise-related conditions to the surrounding noise profile.

Abstract: Methods and systems for optimizing and audio communication system, including detecting a surrounding noise profile, determining one or more states of one or more noise-related conditions corresponding to the surrounding noise profile, and associating the one or more states of the one or more noise-related conditions to the surrounding noise profile.

Abstract: Methods and systems for optimizing and audio communication system, including detecting a surrounding noise profile, determining one or more states of one or more noise-related conditions corresponding to the surrounding noise profile, and associating the one or more states of the one or more noise-related conditions to the surrounding noise profile.

Abstract: A method of providing information storage by means of Automatic Speech Recognition through a communication device of a vehicle comprises establishing a voice communication between an external source and a user of the vehicle, receiving information from the external source, processing the received information using an Automatic Speech Recognition unit in the vehicle and storing the recognized speech in textual form for future retrieval or use.

Abstract: A wireless unit or hub for a vehicle includes a wireless communication interface, such as a Bluetooth® interface. The hub stores profiles for wireless devices that can actively interact with the hub. The wireless devices, which can be cellular phones, headsets, portable music players, and Personal Digital Assistants, are capable of providing audio data, visual data, or both to the hub. The hub monitors for wireless devices in a personal area network of the hub. Based on the profiles, the hub establishes wireless connections with the wireless devices. Using an arbitration scheme, the hub controls the delivery of audio and/or visual data provided by the wireless devices to one or more modules communicatively coupled to the hub. The modules, which can be a user interface module, audio module, visual modules, navigation system, and vehicle entertainment system, are capable of delivering audio, visual, or audio-visual data in the vehicle.

Abstract: An apparatus and method for enhancing a handheld communication device via a telematics system in a vehicle is disclosed. Audio received at the handheld device is transferred to the telematics system in the vehicle. The received audio is then analyzed to determine whether it contains speech, and if so, an audio present signal is generated and the received audio is recorded into a memory coupled to the telematics system. The user can then engage the user interface of the telematics system to replay the recorded audio. Bluetooth protocol is preferably used to establish a channel between the handheld device and the telematics system, which can occur automatically when the two are in proximity. Analysis of the received audio preferably comprises use of a voice detector as part of a speech recognition system otherwise used by the telematics system to assess spoken commands.

Abstract: A method and apparatus for assisting a user in the dialing of a telephone call using voice nametags. A first step includes inputting a telephone number with text. The next steps automatically create a voice nametag from the text for each telephone number using grapheme-to-phoneme conversion. Upon initiation of dialing, a next step enters a spoken phrase, which is then compared to the stored voice nametags. A next step determines a confidence level score of a match between the spoken phrase data and the representations of the stored voice nametags against at least one threshold. A next step selects the stored voice nametag with the best match to the spoken phrase data. A next step provides feedback to the user dependent upon the confidence level of the match, which can include automatically dialing the call if the confidence level is high enough. As part of this last step, an audio feedback tag is generated and stored based on the recognition result passing a confidence threshold criterion.

Abstract: A method and apparatus for adapting a help menu on a user interface, utilizing an input method such as a speech recognition system, for increased efficiency. A list of menu items is presented on the user interface including an optional menu item to reinstate any previously removed menu items. A user selects an item from the menu, such as a help menu, which can then be removed from the list of menu items in accordance with predetermined criteria. The criteria can include how many times the menu item has been accessed and when. In this way, help menu items that are familiar to a user are removed to provide an abbreviated help menu which is more efficient and less frustrating to a user, particularly in a busy and distracting environment such as a vehicle.

Abstract: The present invention provides a method of calculating, within the framework of a speaker dependent system, a standard filler, or garbage model, for the detection of out-of-vocabulary utterances. In particular, the method receives new training data in a speech recognition system (202); calculates statistical parameters for the new training data (204); calculates global statistical parameters based upon the statistical parameters for the new training data (206); and updates a garbage model based upon the global statistical parameters (208). This is carried out on-line while the user is enrolling the vocabulary. The garbage model described in this disclosure is preferably an average speaker model, representative of all the speech data enrolled by the user to date. Also, the garbage model is preferably obtained as a by-product of the vocabulary enrollment procedure and is similar in it characteristics and topology to all the other regular vocabulary HMMs.

Abstract: A method of testing a new vocabulary word is performed using any set of enrollment utterances provided by the user or from an available database. The present method preferably does not use separate training and similarity test utterances. This allows any or all available repetitions of a vocabulary word being enrolled to be used for training (204), therefore improving the robustness of the trained models. Likewise, any or all training repetitions can also be utilized for similarity analysis (212), providing additional test samples which should further improve the detection of acoustically similar words. Additionally, the similarity analysis progresses incrementally and does not need to continue if a confusable word is found. Finally, first and second thresholds could be employed (212, 302) to provide greater flexibility for a user training a speech recognition system.

Abstract: In a statistical based speech recognition system, one of the key issues is the selection of the Hidden Markov Model that best matches a given sequence of feature observations. The problem is usually addressed by the calculation of the maximum likelihood, ML, state sequence by means of a Viterbi or other decoder. Noise or inadequate training can produce a ML sequence associated with a Hidden Markov Model other than the correct model. The method of the present invention provides improved robustness by combining the standard ML state sequence score (416) with an additional path score (418) derived from the dynamics of the ML score as a function of time. These two scores, when combined, form a hybrid metric (420) that, when used with the decoder, optimizes selection of the correct Hidden Markov Model (422).