A prosody analyzer enhances the interpretation of natural language utterances. The analyzer is distributed over a client/server architecture, so that the scope of emotion recognition processing tasks can be allocated on a dynamic basis based on processing resources, channel conditions, client loads etc. The partially processed prosodic data can be sent separately or combined with other speech data from the client device and streamed to a server for a real-time response. Training of the prosody analyzer with real world expected responses improves emotion modeling and the real-time identification of potential features such as emphasis, intent, attitude and semantic meaning in the speaker's utterances.

Emotion detection device and method for use in distributed systems

Sentence based queries from a user are analyzed using a natural language engine to determine appropriate answers from an electronic database. The system and methods are useful for Internet based search engines, as well as distributed speech recognition systems such as a client-server system. The latter are typically implemented on an intranet or over the Internet based on user queries at his/her computer, a PDA, or a workstation using a speech input interface.

System and method for natural language processing of sentence based queries

A voice-operated remote control system has two microphone and an ambient noise remover in a transmitter. One of the microphones picks up a voice command, and the other picks up ambient noise. When the voice command is picked up by one microphone, the ambient noise is also picked up thereby. The ambient noise remover cancels an ambient noise component with an electric signal of the ambient noise picked up by the other microphone, and outputs only a voice command component. The remote control system may have a voice command detector and a mute control circuit. If the preparation of entry of a voice command is detected by the voice command detector, the mute control circuit lowers the sound pressure level of sound reproduced by an information reproducing device before the voice command starts being inputted. The remote control system may also have a recognition condition setting unit for automatically modifying speech recognition conditions if a voice command is rejected to be recognized. The remote control system may further include a speech storage unit for storing input voice commands and a speech reproduction unit for reproducing the stored voice commands as required.

Voice-operated remote control system

A real-time system (100) incorporating speech recognition and linguistic processing for recognizing a spoken query by a user and distributed between client (150) and server (180), is disclosed. The system (100) accepts user's queries in the form of speech at the client (150) where minimal processing extracts a sufficient number of acoustic speech vectors representing the utterance. These vectors are sent via a communications channel (160A) to the server (180) where additional acoustic vectors are derived. Using Hidden Markov Models (HMMs), and appropriate grammars and dictionaries conditioned by the selections made by the user, the speech representing the user's query is fully decoded into text (or some other suitable form) at the server (180). The text corresponding to the user's query is then simultaneously sent to a natural language engine (190) and a database processor (186) where optimized SQL statements are constructed for a full-text search from a database (188) for a record set of several stored questions that best matches the user's query. Further processing in the natural language engine (190) narrows the search to a single stored question. The answer corresponding to this single stored question is next retrieved from the file path and sent to the client (150) in compressed form. At the client (150), the answer to the user's query is articulated to the user using a text-to-speech engine (159) in his or her native natural language. The system (100) requires no training and can operate in several natural languages.

Distributed real time speech recognition system

A real-time speech recognition system includes distributed processing across a client and server for recognizing a spoken query by a user. Both the client and server can dedicate a variable number of processing resources for performing speech recognition functions. The partitioning of responsibility for speech recognition operations can be done on a client by client or connection by connection basis.

Adjustable resource based speech recognition system

A real-time speech processing development system has a control subsystem (CS) and a recognition subsystem (RS) interconnected by a CS/RS interface. The control subsystem includes a control processor, an operator interface, a user interface, and a control program module for loading any one of a plurality of control programs which employ speech recognition processes. The recognition system RS includes a master processor, speech signal processor, and template matching processors all interconnected on a common bus which communicates with the control subsystem through the mediation of the CS/RS interface. The two-part configuration allows the control subsystem to be accessed by the operator for non-real-time system functions, and the recognition subsystem to be accessed by the user for real-time speech processing functions. An embodiment of a speaker verification system includes template enrollment, template training, recognition by template-concatenation and time alignment, silence and filler template generation, and speaker monitoring modes.

Real-time speech processing development system

An apparatus operates to identify the speech signal of an unknown speaker as one of a finite number of speakers. Each speaker is modeled and recognized with any example of their speech. The input to the system is analog speech and the output is a list of scores that measure how similar the input speaker is to each of the speakers whose models are stored in the system. The system includes front end processing means which is responsive to the speech signal to provide digitized samples of the speech signal at an output which are stored in a memory. The stored digitized samples are then retrieved and divided into frames. The frames are processed to provide a series of speech parameters indicative of the nature of the speech content in each of the frames. The processor for producing the speech parameters is coupled to either a speaker modeling means, whereby a model for each speaker is provided and consequently stored, or a speaker recognition mode, whereby the speech parameters are again processed with current parameters and compared with the stored parameters during each speech frame. The comparison is accomplished over a predetermined number of frames whereby a favorable comparison is indicative of a known speaker for which a model is stored.

Multiple parameter speaker recognition system and methods

A method and apparatus for recognizing an unknown speaker from a plurality of speaker candidates. Portions of speech from the speaker candidates and from the unknown speaker are sampled and digitized. The digitized samples are converted into frames of speech, each frame representing a point in an LPC-12 multi-dimensional speech space. Using a character covering algorithm, a set of frames of speech is selected, called characters, from the frames of speech of all speaker candidates. The speaker candidates' portions of speech are divided into smaller portions called segments. A smaller plurality of model characters for each speaker candidate is selected from the character set. For each set of model characters the distance from each speaker candidate's frame of speech to the closest character in the model set is determined and stored in a model histogram. When a model histogram is completed for a segment a distance D is found whereby at least a majority of frames have distances greater D. The mean distance value of D and variance across all segments for both speaker and imposter is then calculated. These values are added to the set of model characters to form the speaker model. To perform recognition the frames of the unknown speaker as they are received are buffered and compared with the sets of model characters to form model histograms for each speaker. A likelihood ratio is formed. The speaker candidate with the highest likelihood ratio is chosen as the unknown speaker.

Edwin H. Wrench

Papers

Real-time speech processing development system

Multiple parameter speaker recognition system and methods

Method and apparatus for text-independent speaker recognition