Abstract: A computationally efficient and robust pitch detection and tracking system and related methods are presented. According to certain exemplary implementations a method is presented comprising identifying an initial set of pitch period candidates using a first estimation algorithm, filtering the initial set of candidates and passing the filtered candidates through a second, more accurate pitch estimation algorithm to generate a final set of pitch period candidates from which the most likely pitch value is selected.

Abstract: A computationally efficient and robust pitch detection and tracking system and related methods are presented. According to certain exemplary implementations a method is presented comprising identifying an initial set of pitch period candidates using a first estimation algorithm, filtering the initial set of candidates and passing the filtered candidates through a second, more accurate pitch estimation algorithm to generate a final set of pitch period candidates from which the most likely pitch value is selected.

Abstract: A computationally efficient and robust pitch detection and tracking system and related methods are presented. According to certain exemplary implementations a method is presented comprising identifying an initial set of pitch period candidates using a first estimation algorithm, filtering the initial set of candidates and passing the filtered candidates through a second, more accurate pitch estimation algorithm to generate a final set of pitch period candidates from which the most likely pitch value is selected.

Abstract: A computationally efficient and robust pitch detection and tracking system and related methods are presented. According to certain exemplary implementations a method is presented comprising identifying an initial set of pitch period candidates using a first estimation algorithm, filtering the initial set of candidates and passing the filtered candidates through a second, more accurate pitch estimation algorithm to generate a final set of pitch period candidates from which the most likely pitch value is selected.

Abstract: A voice message is processed in a distributed system by storing voice message data indicative of a plurality of voice messages on a distributed data store. A distributed data processor accesses the voice messages and extracts desired information from the voice messages. The data processor then augments the data stored in the voice message data store with the extracted information. The user interface component provides user access to the voice messages with the augmented data.

Abstract: The present invention trains a user recognition model for a user. A user enrollment input is received and one or more cohort models are identified from a set of possible cohort models. The cohort models are identified based on a similarity measure between the set of possible cohort models and the user enrollment input. Once the cohort models have been identified, a user model is generated based on data associated with the identified cohort models.

Abstract: A stream of input speech is coupled as an input to a speech recognizer. The speech can be provided to the speech recognizer directly from a user or first stored and provided from a memory circuit. Each input word is recognized by the speech recognizer and a word confidence score is associated with each corresponding recognized word. The recognized words and their associated word confidence scores are provided to a natural language interpreter which parses the stream of recognized words into predetermined edges. From the edges, the natural language interpreter forms semantic slots which represent a semantic meaning. A slot confidence score related to the word or phone confidence scores for each of the words in the slot is determined for each slot. Based upon the slot confidence score, an ancillary application program determines whether to accept the words used to fill each slot.

Abstract: A computationally efficient and robust pitch detection and tracking system and related methods are presented. According to certain exemplary implementations a method is presented comprising identifying an initial set of pitch period candidates using a first estimation algorithm, filtering the initial set of candidates and passing the filtered candidates through a second, more accurate pitch estimation algorithm to generate a final set of pitch period candidates from which the most likely pitch value is selected.

Abstract: In one embodiment, a speech recognition program at a client receives data that is unrecognized, such as an unrecognized word, an unrecognized pronunciation of a known word, an unrecognized dialect of a known, and/or a substantially new word frequency usage. The client transmits the data to a provider, which processes the data into known data, and transmits the known data back to a number of clients, including the client that initially sent the unrecognized data. In one embodiment, the unrecognized data is sent from the client to the provider via a third party, to anonymize the data.