Abstract: The system includes a database of program records representing A/V programs which are available for recording. The system also includes an A/V recording device for receiving a recording command and recording the A/V program. A speech recognizer is provided for receiving the spoken request and translating the spoken request into a text stream having a plurality of words. A natural language processor receives the text stream and processes the words for resolving a semantic content of the spoken request. The natural language processor places the meaning of the words into a task frame having a plurality of key word slots. A dialogue system analyzes the task frame for determining if a sufficient number of key word slots have been filled and prompts the user for additional information for filling empty slots.

Abstract: A speaker authentication system includes a data fuser operable to fuse information to assist in authenticating a speaker providing audio input. In other aspects, the system includes a data store of speaker voiceprints and a voiceprint matching module adapted to receive an audio input and operable to attempt to assist in authenticating a speaker by matching the audio input to at least one of the speaker voiceprints.

Abstract: The call screener employs a telephone system interface connected between a telephone network and a telephone device of a user. The interface selectively routes calls (and refrain from routing calls) based on the results from the dialogue system. The dialogue system elicits speech from an incoming caller and causes the telephone system interface to route calls from the incoming caller based on a comparison of the elicited speech with a set of stored speaker models. The stored speaker models may be maintained automatically by the system, using either a passive mode, in which calls exceeding a predetermined duration are assumed to be “acceptable” callers; and a proactive mode in which the system prompts the user at the end of the call to elect whether to save the speech models developed during that call in the acceptable user database.

Abstract: A constraint-based speech recognition system for use with a form-filling application employed over a telephone system is disclosed. The system comprises an input signal, wherein the input signal includes both speech input and non-speech input of a type generated by a user via a manually operated device. The system further comprises a constraint module operable to access an information database containing information suitable for use with speech recognition, and to generate candidate information based on the non-speech input and the information database, wherein the candidate information corresponds to a portion of the information. The system further comprises a speech recognition module operable to recognize speech based on the speech input and the candidate information. In an exemplary embodiment, the manually operated device is a touch-tone telephone keypad, and the information database is a lexicon encoded according to classes defined by the keys of the keypad.

Abstract: A reduced dimensionality eigenvoice analytical technique is used during training to develop context-dependent acoustic models for allophones. The eigenvoice technique is also used during run time upon the speech of a new speaker. The technique removes individual speaker idiosyncrasies, to produce more universally applicable and robust allophone models. In one embodiment the eigenvoice technique is used to identify the centroid of each speaker, which may then be “subtracted out” of the recognition equation. In another embodiment maximum likelihood estimation techniques are used to develop common decision tree frameworks that may be shared across all speakers when constructing the eigenvoice representation of speaker space.

Abstract: A speech understanding system for receiving a spoken request from a user and processing the request against a knowledge base of programming information for automatically selecting a television program is disclosed. The speech understanding system includes a knowledge extractor for receiving electronic programming guide (EPG) information and processing the EPG information for creating a program database. The system also includes a speech recognizer for receiving the spoken request and translating the spoken request into a text stream having a plurality of words. A natural language processor is provided for receiving the text stream and processing the words for resolving a semantic content of the spoken request. The natural language processor places the meaning of the words into a task frame having a plurality of key word slots.

Abstract: An automated hotel attendant is provided for coordinating room-to-room calling over a telephone switching system that supports multiple telephone extensions. A hotel registration system receives and stores the spelled names of hotel guests as well as assigns each guest an associated telephone extension. A lexicon training system is connected to the hotel registration system for generating pronunciations for each spelled name by converting the characters that spell those names into word-phoneme data. This word-phoneme data is in turn stored in a lexicon that is used by a speech recognition system. In particular, a phoneticizer in conjunction with a Hidden Markov Model (HMM) based model trainer serves as the basis for the lexicon training system, such that one or several HMM models associated with each guest name are stored in the lexicon.

Abstract: 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.

Abstract: New entries are added to the lexicon by entering them as spelled words. A transcription generator, such as a decision-tree-based phoneme or morpheme transcription generator, converts each spelled word into a set of n-best transcriptions or sequences. Meanwhile, user input or automatically generated speech corresponding to the spelled word is processed by an automatic speech recognizer and the recognizer rescores the transcriptions or sequences produced by the transcription generator. One or more of the highest scored (highest confidence) transcriptions may be added to the lexicon to update it. If desired, the spelled word-pronunciation pairs generated by the system can be used to retrain the transcription generator, making the system adaptive or self-learning.

Abstract: Decision trees are used to store a series of yes-no questions that can be used to convert spelled-word letter sequences into pronunciations. Letter-only trees, having internal nodes populated with questions about letters in the input sequence, generate one or more pronunciations based on probability data stored in the leaf nodes of the tree. The pronunciations may then be improved by processing them using mixed trees which are populated with questions about letters in the sequence and also questions about phonemes associated with those letters. The mixed tree screens out pronunciations that would not occur in natural speech, thereby greatly improving the results of the letter-to-pronunciation transformation.

Abstract: The mixed decision tree includes a network of yes-no questions about adjacent letters in a spelled word sequence and also about adjacent phonemes in the phoneme sequence corresponding to the spelled word sequence. Leaf nodes of the mixed decision tree provide information about which phonetic transcriptions are most probable. Using the mixed trees, scores are developed for each of a plurality of possible pronunciations, and these scores can be used to select the best pronunciation as well as to rank pronunciations in order of probability. The pronunciations generated by the system can be used in speech synthesis and speech recognition applications as well as lexicography applications.