Abstract: A method and apparatus for data entry by voice under adverse conditions is disclosed. More specifically it provides a way for efficient and robust form filling by voice. A form can typically contain one or several fields that must be filled in. The user communicates to a speech recognition system and word spotting is performed upon the utterance. The spotted words of an utterance form a phrase that can contain field-specific values and/or commands. Recognized values are echoed back to the speaker via a text-to-speech system. Unreliable or unsafe inputs for which the confidence measure is found to be low (e.g. ill-pronounced speech or noises) are rejected by the spotter. Speaker adaptation is furthermore performed transparently to improve speech recognition accuracy. Other input modalities can be additionally supported (e.g. keyboard and touch-screen). The system maintains a dialogue history to enable editing and correction operations on all active fields.

Abstract: A common requirement in automatic speech recognition is to recognize a set of words for any speaker without training the system for each new speaker. A speech recognition system is provided utilizing linear discriminant based phonetic similarities with inter-phonetic unit value normalization. Linear discriminant analysis is utilized using training data with both in-class and out-class sample training utterances for generating linear discriminant vectors for each of the phonetic units. The dot product of each linear discriminant vector and the time spectral pattern vectors generated from the input speech are computed. The resultant raw similarity vectors are then normalized utilizing normalization look-up tables for providing similarity vectors which are utilized by a word matcher for word recognition.

Abstract: A method and system for spotting words in a speech signal having adverse and unknown noisy environments is provided. The method removes the dynamic bias introduced by the environment (i.e., noise and channel effect) that is specific to each word of the lexicon. The method includes the step of generating a first recognition score based on the speech signal and a lexicon entry for a word. The recognition score tracks an absolute likelihood that the word is in the speech signal. A background score is estimated based on the first recognition score. The method further provides for calculating a confidence score based on a matching ratio between a minimum recognition value and the background score. The method and system can be implemented for any number of words, depending upon the application. The confidence scores therefore track noise-corrected likelihoods that the words are in the speech signal.

Abstract: A navigation apparatus is disclosed which may be used by law enforcement personnel for rapid intervention to a location while adding safety and reliability to the process. The apparatus includes a computer system, having an operating system, memory and a user interface. The system further includes a positioning system, such as a GPS system for determining the position of a vehicle. The positioning system communicates with the operating system. An information database, communicating with the operating system, contains data related to routing information concerning routes for travel by the vehicle. The routing information includes safety information concerning route safety in the traveling region accessible by the vehicle. The apparatus further includes a routing system in communication with the operating system that determines a route based at least in part on the routing information.

Abstract: A navigation apparatus is disclosed which may be used by law enforcement personnel for rapid intervention to a location while adding safety and reliability to the process. The apparatus includes a computer system, having an operating system, memory and a user interface. The system further includes a positioning system, such as a GPS system for determining the position of a vehicle. The positioning system communicates with the operating system. An information database, communicating with the operating system, contains data related to routing information concerning routes for travel by the vehicle. The routing information includes safety information concerning route safety in the traveling region accessible by the vehicle. The apparatus further includes a routing system in communication with the operating system that determines a route based at least in part on the routing information.

Abstract: A method and apparatus for data entry by voice under adverse conditions is disclosed. More specifically it provides a way for efficient and robust form filling by voice. A form can typically contain one or several fields that must be filled in. The user communicates to a speech recognition system and word spotting is performed upon the utterance. The spotted words of an utterance form a phrase that can contain field-specific values and/or commands. Recognized values are echoed back to the speaker via a text-to-speech system. Unreliable or unsafe inputs for which the confidence measure is found to be low (e.g. ill-pronounced speech or noises) are rejected by the spotter. Speaker adaptation is furthermore performed transparently to improve speech recognition accuracy. Other input modalities can be additionally supported (e.g. keyboard and touch-screen). The system maintains a dialogue history to enable editing and correction operations on all active fields.

Abstract: A common requirement in automatic speech recognition is to recognize a set of words for any speaker without training the system for each new speaker. A speech recognition system is provided utilizing linear discriminant based phonetic similarities with inter-phonetic unit value normalization. Linear discriminant analysis is utilized using training data with both in-class and out-class sample training utterances for generating linear discriminant vectors for each of the phonetic units. The dot product of each linear discriminant vector and the time spectral pattern vectors generated from the input speech are computed. The resultant raw similarity vectors are then normalized utilizing normalization look-up tables for providing similarity vectors which are utilized by a word matcher for word recognition.

Abstract: A method and system for spotting words in a speech signal having adverse and unknown noisy environments is provided. The method removes the dynamic bias introduced by the environment (i.e., noise and channel effect) that is specific to each word of the lexicon. The method includes the step of generating a first recognition score based on the speech signal and a lexicon entry for a word. The recognition score tracks an absolute likelihood that the word is in the speech signal. A background score is estimated based on the first recognition score. The method further provides for calculating a confidence score based on a matching ratio between a minimum recognition value and the background score. The method and system can be implemented for any number of words, depending upon the application. The confidence scores therefore track noise-corrected likelihoods that the words are in the speech signal.

Abstract: A digital word prototype is constructed using one or more speech utterance for a given spoken word or phrase. First, a phone model is used to derive phoneme similarity time series for each of a plurality of phonemes which represent the degree of similarity between the speech utterance and a set of standard phonemes contained in the phone model. Next, the phoneme similarity data is normalized in relation to a non-speech part of the input speech signal. The normalized phoneme similarity data is divided into segments, such that the sum of all normalized phoneme similarity values in a segment are equal for each segment. Next, a word model is constructed from the phoneme similarity data. To do so, within each segment, a summation value is determined by summing over speech frames each of the normalized phoneme similarity values associated with a particular phoneme. In this way, the word model is represented by a vector of summation values that compactly correlate to the normalized phoneme similarity data.

Abstract: The voice dialing server plugs into one or more unused extensions of a branch exchange system to provide each of the users on the system with voice dialing services. To use the system a user simply dials the extension to which the server is attached. The server then prompts the user to supply the name of a party to be called. The name is then looked up in a telephone number dictionary unique to that user. The system then places the telephone call by sending commands to the branch exchange system that simulate the operations a user would perform to connect to an outside line or inside extension and then place the call. The server incorporates a speech processing module having a multistage word recognizer that represents speech in terms of high phoneme similarity values. This representation is highly compact, allowing the word recognizer to perform the recognizer and fine match stages with far less processor overhead than frame-by-frame speech recognizers.

Abstract: The multimodal telephone prompts the user using both a visual display and synthesized voice. It receives user input via keypad and programmable soft keys associated with the display, and also through user-spoken commands. The voice module includes a two stage speech recognizer that models speech in terms of high similarity values. A dialog manager associated with the voice module maintains the visual and verbal systems in synchronism with one another. The dialog manager administers a state machine that records the dialog context. The dialog context is used to ensure that the appropriate visual prompts are displayed--showing what commands are possible at any given point in the dialog. The speech recognizer also uses the dialog context to select the recognized word candidate that is appropriate to the current context.

Abstract: The word hypothesizer reduces the search space for more computationally expensive word recognizers. Each periodic interval of input speech is represented as a vector of phoneme similarity values from which the high similarity regions are selected and parameterized. The hypothesizer computes alignment parameters for each of a plurality of previously stored word prototypes, vis-a-vis the high similarity regions of the input speech utterance. Those word prototypes having the highest recognition scores are selected as word candidates for the fine match recognizer.

Abstract: The multistage word recognizer uses a word reference representation based on reliably detected peaks of phoneme similarity values. The word reference representation captures the basic features of the words by targets that describe the location and shape of stable peaks of phoneme similarity values. The first stage of the word hypothesizer represents each reference word with statistical information on the number of high similarity regions over a predefined number of time intervals. The second stage represents each word by a prototype that consists of a series of phoneme targets and global statistics, namely the average word duration and average match rate. These represent the degree of fit of the word prototype to its training data. Word recognition scores generated in the two stages are converted to dimensionless normalized values and combined by averaging for use in selecting the most probable word candidates.

Abstract: Digitized speech utterances are converted into phoneme similarity data and regions of high similarity are then extracted and used in forming the word prototype. By alignment across speakers unreliable high phoneme similarity regions are eliminated. Word prototype targets are then constructed comprising the following parameters: the phoneme symbol, the average peak height of the phoneme similarity score, the average peak location and the left and right frame locations. For each target a statistical weight is assigned representing the percentage of occurrences the particular high similarity region occurred across all speakers. The word prototype is feature-based allowing a robust speech representation to be constructed without the need for frame-by-frame analysis.